Dissertations / Theses on the topic 'Predictive and Adaptive Control'
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Yoon, Tae-Woong. "Robust adaptive predictive control." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359527.
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MeVay, Alex C. H. (Alex Craige Haviland) 1979. "Predictive comparators with adaptive control." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29654.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 72).
A linear predictor and adaptive control loop are added to a conventional comparator to greatly reduce the delay. A linear predictor feeds an estimated future signal to the comparator to compensate for the comparator's internal delay. On a cycle-by-cycle basis, an adaptive controller adjusts the comparator's bias current to null the error. Emphasis is placed on low power consumption, including the development of a linear predictor with no static power consumption. Improvements of two orders of magnitude in power-delay product are demonstrated. The adaptive comparator is ideally suited for applications such as synchronous rectification but will also find broad applicability anywhere an asynchronous comparator is required, such as sensor interfaces, oscilloscope triggers, and some types of analog-digital converters.
by Alex C.H. MeVay.
M.Eng.
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Brodie, K. A. "Inferential predictive control." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310173.
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Elshafei, Abdel-Latif. "Adaptive predictive control : analysis and expert implementation." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30802.
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A generalized predictive controller has been derived based on a general state-space model. The case of a one-step control horizon has been analyzed and its equivalence to a perturbation problem has been emphasized. In the case of a small perturbation, the closed-loop poles have been calculated with high accuracy. For the case of a general perturbation, an upper bound on the permissible perturbation norm has been derived. A functional analysis approach has also been adopted to assess the closed-loop stability in the case of nonlinear systems. Both the plant-model match and plant-model mismatch cases have been analyzed. The proposed controller has proven to be so robust that an adaptive implementation based on Laguerre-filter modelling has been motivated. Both SISO and MIMO schemes have been analyzed. Using a sufficient number of Laguerre filters for modelling, the adaptive controller has been proven to be globally convergent. For low-order models, the robustness of the adaptive controller can be insured by increasing the prediction horizon. The convergence and robustness results have been extended to other predictive controllers. A comparative study has shown that the proposed controller would be superior to the other predictive controllers if the open-loop system is stable, well-damped, and of unknown order or time delay. To achieve a reliable control without deep user involvement, the adaptive version of the proposed controller has been implemented using the expert shell, G2. The resulting expert system has been used to orchestrate the operation of the controller, provide an interactive user interface, adjust the Laguerre-filter model using AI search algorithms, and evaluate the performance of the controller on-line. Based on the performance evaluation, the tuning parameters of the controller can be adjusted on-line using fuzzy-logic rules.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Fun, Wey. "Adaptive motor control using predictive neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/31065.
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Eure, Kenneth W. II. "Adaptive Predictive Feedback Techniques for Vibration Control." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30342.
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In this dissertation, adaptive predictive feedback control is used to suppress plate vibrations. The adaptive predictive controller consists of an on-line identification technique coupled with a control scheme. Various system identification techniques are investigated and implemented including batch least squares, projection algorithm, and recursive least squares. The control algorithms used include Generalized Predictive Control and Deadbeat Predictive Control. This dissertation combines system identification and control to regulate broadband disturbances in modally-dense structures. As it is assumed that the system to be regulated is unknown or time varying, the control schemes presented in this work have the ability to identify and regulate a plant with only an initial estimate of the system order. In addition, theoretical development and experimental results presented in this work confirm the fact that an adaptive controller operating in the presence of disturbances will automatically incorporate an internal noise model of the disturbance perturbing the plant if the system model order is chosen sufficiently large. It is also shown that the adaptive controller has the ability to track changes in the disturbance spectrum as well as track a time varying plant under certain conditions. This work presents a broadband multi-input multi-output control scheme which utilizes both the DSP processor and the PC processor in order to handle the computational demand of broadband regulation of a modally-dense plant. Also, the system identification technique and the control algorithm may be combined to produce a direct adaptive control scheme which estimates the control parameters directly from input and output data. Experimental results for various control techniques are presented using an acoustic plant, a rectangular plate with clamped boundary conditions, and a time varying plate.Ph. D.
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Sheth, Katha Janak. "Model predictive control for adaptive digital human modeling." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/884.
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We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.
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Peng, Youbin. "On adaptive control :Pole-zero placement control and generalized predictive control." Doctoral thesis, Universite Libre de Bruxelles, 1991. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/213050.
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Lambert, Martin Richard. "Adaptive control of flexible systems." Thesis, University of Oxford, 1987. http://ora.ox.ac.uk/objects/uuid:d19d44f9-b7db-4b00-95be-4cf897450836.
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This thesis reports the successful application of the recently introduced Generalised Predictive Control self-tuner to the high-performance positioning of a real flexible single-link robot arm. The large amount of experimental time available on this high bandwidth system allowed exhaustive testing of the 'tuning-knobs' and 'design-filters' available to the user for tailoring the closed-loop. Based upon these experiments a coherent philosophy for configuring GPC in practice is generated. Configuration details found to be necessary for satisfactory GPC control of this high-order neutrally stable and non-minimum-phase plant, with its lightly damped resonant modes, are isolated. In particular it is found that band-pass filtering of data is essential for stable offset-free control using finite-order models of the plant. These aspects are considered in detail both theoretically and experimentally. In this application, as is often the case in practice, some information about the plant dynamics is available beforehand. Novel methods for the inclusion of this prior knowledge are introduced and their beneficial effects on the convergence of the recursive least squares estimation scheme, upon which most self-tuners are based, are demonstrated in simulation and experiment. A novel high-speed 68010/20 multi-processor computer system is described which allows the implementation of GPC at the required high sample rate (60Hz). The software division of the self-tuning algorithm into concurrently and sequentially executing tasks is discussed in detail.
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Brugnolli, Mateus Mussi. "Predictive adaptive cruise control in an embedded environment." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-24092018-151311/.
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The development of Advanced Driving Assistance Systems (ADAS) produces comfort and safety through the application of several control theories. One of these systems is the Adaptive Cruise Control (ACC). In this work, a distribution of two control loops of such system is developed for an embedded application to a vehicle. The vehicle model was estimated using the system identification theory. An outer loop control manages the radar data to compute a suitable cruise speed, and an inner loop control aims for the vehicle to reach the cruise speed given a desired performance. For the inner loop, it is used two different approaches of model predictive control: a finite horizon prediction control, known as MPC, and an infinite horizon prediction control, known as IHMPC. Both controllers were embedded in a microcontroller able to communicate directly with the electronic unit of the vehicle. This work validates its controllers using simulations with varying systems and practical experiments with the aid of a dynamometer. Both predictive controllers had a satisfactory performance, providing safety to the passengers.A inclusão de sistemas avançados para assistência de direção (ADAS) tem beneficiado o conforto e segurança através da aplicação de diversas teorias de controle. Um destes sistemas é o Sistema de Controle de Cruzeiro Adaptativo. Neste trabalho, é usado uma distribuição de duas malhas de controle para uma implementação embarcada em um carro de um Controle de Cruzeiro Adaptativo. O modelo do veículo foi estimado usando a teoria de identificação de sistemas. O controle da malha externa utiliza dados de um radar para calcular uma velocidade de cruzeiro apropriada, enquanto o controle da malha interna busca o acionamento do veículo para atingir a velocidade de cruzeiro com um desempenho desejado. Para a malha interna, é utilizado duas abordagens do controle preditivo baseado em modelo: um controle com horizonte de predição finito, e um controle com horizonte de predição infinito, conhecido como IHMPC. Ambos controladores foram embarcados em um microcontrolador capaz de comunicar diretamente com a unidade eletrônica do veículo. Este trabalho valida estes controladores através de simulações com sistemas variantes e experimentos práticos com o auxílio de um dinamômetro. Ambos controladores preditivos apresentaram desempenho satisfatório, fornecendo segurança para os passageiros.
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Lopez, Brett Thomas. "Adaptive robust model predictive control for nonlinear systems." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122395.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-124).
Modeling error and external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over control policies but at the expense of computational complexity. An alternative strategy, known as tube MPC, uses a robust controller (designed offline) to keep the system in an invariant tube centered around a desired nominal trajectory (generated online). While tube MPC regains tractability, there are several theoretical and practical problems that must be solved for it to be used in real-world scenarios. First, the decoupled trajectory and control design is inherently suboptimal, especially for systems with changing objectives or operating conditions. Second, no existing tube MPC framework is able to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. And third, the inability to reduce state-dependent uncertainty through online parameter adaptation/estimation leads to systematic error in the trajectory design. This thesis aims to address these limitations by developing a computationally tractable nonlinear tube MPC framework that is applicable to a broad class of nonlinear systems.
"This work was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1122374, by the DARPA Fast Lightweight Autonomy (FLA) program, by the NASA Convergent Aeronautics Solutions project Design Environment for Novel Vertical Lift Vehicles (DELIVER), and by ARL DCIST under Cooperative Agreement Number W911NF- 17-2-0181"--Page 7.
by Brett T. Lopez.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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Morinelly, Sanchez Juan Eduardo. "Adaptive Model Predictive Control with Generalized Orthonormal Basis Functions." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1091.
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An adaptive model predictive control (MPC) method using models derived from orthonormal basis functions is presented. The defining predictor dynamics are obtained from state-space realizations of finite truncations of generalized orthonormal basis functions (GOBF). A structured approach to define multivariable system models with customizable, open-loop stable linear dynamics is presented in Chapter 2. Properties of these model objects that are relevant to the adaptation component of the overall scheme, are also discussed. In Chapter 3, non-adaptive model predictive control policies are presented with the definition of extended state representations through filter operations that enable output feedback. An infinite horizon set-point tracking policy which always exists under the adopted modeling framework is presented. This policy and its associated cost are included as the terminal stage elements for a more general constrained case. The analysis of robust stability guarantees for the non-adaptive constrained formulation is presented, under the assumption of small prediction errors. In Chapter 4, adaptation is introduced and the certainty equivalence constrained MPC policy is formulated under the same framework of its non-adaptive counterpart. Information constraints that induce the excitation of the signals relevant to the adaptation process are formulated in Chapter 5. The constraint generation leverages the GOBF model structure by enforcing a sufficient richness condition directly on the state elements relevant to the control task. This is accomplished by the definition of a selection procedure that takes into account the characteristics of the most current parameter estimate distribution. Throughout the manuscript, illustrative simulation examples are provided with respect to minimal plant models. Concluding remarks and general descriptions for potential future work are outlined in Chapter 6.
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Grenholm, Sven. "Adaptive Model Predictive Control for Reference Tracking Vehicle Motion." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286337.
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This thesis report presents a set of One-Dimensional Vehicle Motion Reference Tracking control algorithms for vehicle motion along a predetermined path. A general physical model of the motion dynamics of a vehicle along a predetermined path is presented. From this physical model a discretized linearized prediction model is derived, and this prediction model is utilized to formulate a Quadratic Programming optimization problem. This serves as the basis for a receding horizon Model Predictive Control algorithm. The model predictive controller is augmented with a parallel Recursive Least Square Error identification algorithm used to regularly reestimate the mass parameter of the prediction model to handle any wrongful model assumptions. These algorithms are to be used for tracking position and velocity references. Evaluation of the algorithms is performed in simulation. The presented algorithms are shown to be accurate and robust. Specific problematic edge cases where performance is compromised are shown and suggestions on how to tackle them are provided.Detta examensarbete presenterar ett antal styralgoritmer för referensföljande endimensionell fordonsrörelse. En fysisk modell för ett fordons rörelsedynamik presenteras längs en förbestämd bana. Utifrån denna modell härleds en diskretiserad linjariserad prediktionsmodell. Denna prediktionsmodell används för att formulera ett K vadratiskt Programmerings-problem. Detta optimeringsproblem står till grund för en model-prediktiv regleralgoritm. Detta reglersystem augumenteras med en rekursiv minsta-kvadrat-fels algoritm för systemidentifiering, som används till att upprepande återuppskatta massan för att hantera systematiska fel i prediktionsmodellen. Dessa algoritmer används till referensföljning i position och hastighet. Utvärderingen av algoritmerna genomförs i simulation. De presenterade algoritmerna uppvisas att vara generellt sett träffsäkra och robusta. Specifika problematiska fall där prestandan blir sämre lyfts upp och förslag på hur dessa scenarion skulle kunna hanteras medföljer.
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Andina, Elisa. "Complexity and Conservatism in Linear Robust Adaptive Model Predictive Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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Questa tesi presenta uno schema di controllo robusto adattativo basato sulla tecnica di controllo avanzato model predictive control (MPC) per sistemi lineari soggetti a disturbi additivi e incertezze parametriche, costanti e variabili. L'approccio proposto fornisce uno schema di controllo efficiente dal punto di vista computazionale con stima dei parametri online per ottenere un aumento delle prestazioni e una diminuzione progressiva del conservatismo. L'insieme dei parametri è estimato usando una tecnica di identificazione a finestra mobile per ottenere un insieme con complessità limitata. Il soddisfacimento robusto dei vincoli è ottenuto tramite la tecnica di controllo robusto tube based MPC, mentre la stabilità L2 dello schema ad anello chiuso è assicurata utilizzando una stima dei parametri ottenuta con l'algoritmo least mean squares (LMS) nella funzione di costo. Con un esempio infine viene studiato il compromesso tra complessità e conservatismo di tale schema di controllo efficiente dal punto di vista computazionale.
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Wang, Shensheng. "Weighting normalization in optimal predictive control /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025659.
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Coca, Diana Simona. "Adaptive generalised predictive control applied to low-flow inhalational anaesthesia." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401186.
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Ryan, Timothy Patrick. "Model Predictive Adaptive Cruise Control with Consideration of Comfort and Energy Savings." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103744.
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The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger.
The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle.Master of Science
The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 Chevrolet Blazer into a hybrid. This modification is accomplished by creating a vehicle that burns less gasoline and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of Adaptive Cruise Control (ACC). Initially, research is conducted on various control strategies that provide the necessary functionality. A controller that predicts future events is selected for the Adaptive Cruise Control. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy consumption savings to the rider while maintaining safety as the priority. Rider comfort is achieved by creating a smoother ride. Lastly, a proper energy analysis showcases the potential energy savings with the implementation of the Adaptive Cruise Control system. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified vehicle model to simulate different conditions. The city simulations of a traditional ACC system show a 14% reduction in energy at the wheel. City simulations of the environmentally friendly Adaptive Cruise Controller show a 29% reduction in energy. Both of these simulations allow for comfortable ride. Specifically, maximum car jerk is reduced by 90%. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces energy consumption at the wheel while improving rider comfort.
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Lambert, E. P. "Process control applications of long-range prediction." Thesis, University of Oxford, 1987. http://ora.ox.ac.uk/objects/uuid:de56df0b-466c-42ce-a03b-72228ad6af2a.
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The recent Generalised Predictive Control algorithm (Clarke et al, 1984,87) is a self-tuning/ adaptive control algorithm that is based upon long-range prediction, and is thus claimed to be particularly suitable for process control application. The complicated nature of GPC prevents the application of standard analytical techniques. Therefore an alternative technique is developed where an equivalent closed loop expression is repeatedly calculated for various control scenarios. The properties of GPC are investigated and, in particular, it is shown that 'default' values for GPC's design parameters give a mean-level type of control law that can reasonably be expected to provide robust control for a wide variety of processes. Two successful industrial applications of GPC are then reported. The first series of trials involve the SISO control of soap moisture for a full-scale drying process. After a brief period of PRBS assisted self-tuning default GPC control performance is shown to be significantly better than the existing manual control, despite the presence of a large time-delay, poor measurements and severe production restrictions. The second application concerns the MIMO inner loop control of a spray drying tower using two types of GPC controller: full multivariable MGPC, and multi-loop DGPC. Again after only a brief period of PRBS assisted self-tuning both provide dramatically superior control compared to the existing multi-loop gain-scheduled PID control scheme. In particular the use of MGPC successfully avoids any requirement for a priori knowledge of the process time-delay structure or input-output pairing. The decoupling performance of MGPC is improved by scaling and that of DGPC by the use of feed-forward. The practical effectiveness of GPC's design parameters (e.g. P, T and λ) is also demonstrated. On the estimation side of adaptive control the current state-of-the-art algorithms are reviewed and shown to suffer from problems such as 'blowup', parameter drift and sensitivity to unmeasurable load disturbances. To overcome these problems two novel estimation algorithms (CLS and DLS) are developed that extend the RLS cost-function to include weighting of estimated parameters. The exploitation of the 'fault detection' properties of CLS is proposed as a more realistic estimation philosophy for adaptive control than the 'continuous retention of adaptivity'.
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Martínez, Iván García. "Indirect adaptive predictive control applied to an industrial tank level plant." Instituto Tecnológico de Aeronáutica, 2011. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=1550.
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Predictive control algorithms have found growing acceptance in academic and industrial applications. However, as every control methodology, it has drawbacks, which is the need for an appropriate process model to be available. Therefore, the controller may become limited in applications with nonlinear and time-varying dynamics. When the predictions are not satisfactory due to an inappropriate model, it would be convenient to have an adaptive mechanism to match the process model and the process dynamics. In this work, SISO and MIMO formulations of indirect adaptive predictive control are implemented online to control an industrial tank level plant where process and heating tanks are operated. The experiments were carried out in conditions that are typical of an industrial situation, where the plant is nonlinear, multivariable and time varying. Results from the simulations and from the online implementation showed that the predictive control algorithm is limited when its predictive model is not representative and by implementing an adaptive mechanism these limitations have been overcome. Therefore, results show that the adaptive predictive controller outperforms the predictive controller when operating the industrial tank level plant, highlighting its potential as a solution for industrial control problems.
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Hernandez, Vicente Bernardo Andres. "Model predictive control for linear systems : adaptive, distributed and switching implementations." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/22174/.
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Thanks to substantial past and recent developments, model predictive control has become one of the most relevant advanced control techniques. Nevertheless, many challenges associated to the reliance of MPC on a mathematical model that accurately depicts the controlled process still exist. This thesis is concerned with three of these challenges, placing the focus on constructing mathematically sound MPC controllers that are comparable in complexity to standard MPC implementations. The first part of this thesis tackles the challenge of model uncertainty in time-varying plants. A new dual MPC controller is devised to robustly control the system in presence of parametric uncertainty and simultaneously identify more accurate representations of the plant while in operation. The main feature of the proposed dual controller is the partition of the input, in order to decouple both objectives. Standard robust MPC concepts are combined with a persistence of excitation approach that guarantees the closed-loop data is informative enough to provide accurate estimates. Finally, the adequacy of the estimates for updating the MPC's prediction model is discussed. The second part of this thesis tackles a specific type of time-varying plant usually referred to as switching systems. A new approach to the computation of dwell-times that guarantee admissible and stable switching between mode-specific MPC controllers is proposed. The approach is computationally tractable, even for large scale systems, and relies on the well-known exponential stability result available for standard MPC controllers. The last part of this thesis tackles the challenge of MPC for large-scale networks composed by several subsystems that experience dynamical coupling. In particular, the approach devised in this thesis is non-cooperative, and does not rely on arbitrarily chosen parameters, or centralized initializations. The result is a distributed control algorithm that requires one step of communication between neighbouring subsystems at each sampling time, in order to properly account for the interaction, and provide admissible and stabilizing control.
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Ajibulu, Ayodeji Opeoluwa. "Robust adaptive model predictive control for intelligent drinking water distribution systems." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8193/.
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Large-scale complex systems have large numbers of variables, network structure of interconnected subsystems, nonlinearity, spatial distribution with several time scales in its dynamics, uncertainties and constrained. Decomposition of large-scale complex systems into smaller more manageable subsystems allowed for implementing distributed control and coordinations mechanisms. This thesis proposed the use of distributed softly switched robustly feasible model predictive controllers (DSSRFMPC) for the control of large-scale complex systems. Each DSSRFMPC is made up of reconfigurable robustly feasible model predictive controllers (RRFMPC) to adapt to different operational states or fault scenarios of the plant. RRFMPC reconfiguration to adapt to different operational states of the plant is achieved using the soft switching method between the RRFMPC controllers. The RRFMPC is designed by utilizing the off-line safety zones and the robustly feasible invariant sets in the state space which are established off-line using Karush Kuhn Tucker conditions. This is used to achieve robust feasibility and recursive feasibility for the RRFMPC under different operational states of the plant. The feasible adaptive cooperation among DSSRFMPC agents under different operational states are proposed. The proposed methodology is verified by applying it to a simulated benchmark drinking water distribution systems (DWDS) water quality control.
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Hu, Jian-Quan. "Adaptive fuzzy predictive control using a neuro-fuzzy model with application to sintering." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265575.
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Cho, Sukhwan. "A Learning Control, Intervention Strategy for Location-Aware Adaptive Vehicle Dynamics Systems." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/74422.
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The focus of Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) research is to develop a system to avoid situations in which the vehicle exceeds its handling capabilities. The proposed method is predictive, estimating the ability of the vehicle to successfully navigate upcoming terrain, and it is assumed that the future vehicle states and local driving environment is known. An Intervention Strategy must be developed such that the vehicle is navigated successfully along a road via modest changes to the driver's commands and do so in a manner that is in harmony with the driver's intentions and not in a distracting or irritating manner. Clearly this research relies on the numerous new technologies being developed to capture and convey information about the local driving environment (e.g., bank angle, elevation changes, curvature, and friction coefficient) to the vehicle and driver.Ph. D.
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Lloyd, John William. "Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/29306.
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A method to adapt the Generalized Predictive Control parameters to improve broadband disturbance rejection was developed and tested. The effect of the parameters on disturbance rejection has previously been poorly understood and a trial and error method was used to achieve adequate results. This dissertation provides insight on the effect of the parameters, as well as an adaptive tuning method to adjust them.
The study begins by showing the effect of the four GPC parameters, the control and prediction horizons, control weighting &lambda , and order, on the disturbance rejection and control effort of a vibrating plate. It is shown that the effect of increases in the control and prediction horizon becomes negligible after a certain point. This occurs at nearly the same point for a variety of &lambda 's and orders, and hence they can be eliminated from the tuning space.
The control effort and closed-loop disturbance rejection are shown to be highly dependant on &lambda and order, thereby becoming the parameters that need to be tuned. The behavior is categorized into various groups and further investigated. The pole and zero locations of the closed-loop system are examined to reveal how GPC gains control and how it can fail for non-minimum phase plants.
A set of fuzzy logic modules is developed to adapt &lambda with order fixed, and conversely to adapt order with &lambda fixed. The effectiveness of the method is demonstrated in both numerical simulations and laboratory experiments.Ph. D.
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Terry, Jonathan Spencer. "Adaptive Control for Inflatable Soft Robotic Manipulators with Unknown Payloads." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6769.
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Soft robotic platforms are becoming increasingly popular as they are generally safer, lighter, and easier to manufacture than their more rigid, heavy, traditional counterparts. These soft platforms, while inherently safer, come with significant drawbacks. Their compliant components are more difficult to model, and their underdamped nature makes them difficult to control. Additionally, they are so lightweight that a payload of just a few pounds has a significant impact on the manipulator dynamics. This thesis presents novel methods for addressing these issues. In previous research, Model Predictive Control has been demonstrably useful for joint angle control for these soft robots, using a rigid inverted pendulum model for each link. A model describing the dynamics of the entire arm would be more desirable, but with high Degrees of Freedom it is computationally expensive to optimize over such a complex model. This thesis presents a method for simplifying and linearizing the full-arm model (the Coupling-Torque method), and compares control performance when using this method of linearization against control performance for other linearization methods. The comparison shows the Coupling-Torque method yields good control performance for manipulators with seven or more Degrees of Freedom. The decoupled nature of the Coupling-Torque method also makes adaptive control, of the form described in this thesis, easier to implement. The Coupling-Torque method improves performance when the dynamics are known, but when a payload of unknown mass is attached to the end effector it has a significant impact on the dynamics. Adaptive Control is needed at this point to compensate for the model's poor approximation of the system. This thesis presents a method of layering Model Reference Adaptive Control in concert with Model Predictive Control that improves control performance in this scenario. The adaptive controller modifies dynamic parameters, which are then delivered to the optimizer, which then returns inputs for the system that take all of this information into account. This method has been shown to reduce step input tracking error by 50% when implemented on the soft robot.
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Castillo, Carlos L. "Fault-tolerant adaptive model predictive control using joint kalman filter for small-scale helicopter." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002711.
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Cho, B. "Control of a hybrid electric vehicle with predictive journey estimation." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2589.
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Battery energy management plays a crucial role in fuel economy improvement of
charge-sustaining parallel hybrid electric vehicles. Currently available control strategies
consider battery state of charge (SOC) and driver’s request through the pedal input in
decision-making. This method does not achieve an optimal performance for saving fuel
or maintaining appropriate SOC level, especially during the operation in extreme
driving conditions or hilly terrain. The objective of this thesis is to develop a control
algorithm using forthcoming traffic condition and road elevation, which could be fed
from navigation systems. This would enable the controller to predict potential of
regenerative charging to capture cost-free energy and intentionally depleting battery
energy to assist an engine at high power demand.
The starting point for this research is the modelling of a small sport-utility vehicle by
the analysis of the vehicles currently available in the market. The result of the analysis
is used in order to establish a generic mild hybrid powertrain model, which is
subsequently examined to compare the performance of controllers. A baseline is
established with a conventional powertrain equipped with a spark ignition direct
injection engine and a continuously variable transmission. Hybridisation of this vehicle
with an integrated starter alternator and a traditional rule-based control strategy is
presented. Parameter optimisation in four standard driving cycles is explained, followed
by a detailed energy flow analysis.
An additional potential improvement is presented by dynamic programming (DP),
which shows a benefit of a predictive control. Based on these results, a predictive
control algorithm using fuzzy logic is introduced. The main tools of the controller
design are the DP, adaptive-network-based fuzzy inference system with subtractive
clustering and design of experiment. Using a quasi-static backward simulation model,
the performance of the controller is compared with the result from the instantaneous
control and the DP. The focus is fuel saving and SOC control at the end of journeys,
especially in aggressive driving conditions and a hilly road. The controller shows a
good potential to improve fuel economy and tight SOC control in long journey and hilly
terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap
between the baseline controller and the DP. However, there is little benefit in short trips
and flat road. It is caused by the low improvement margin of the mild hybrid powertrain
and the limited future journey information.
To provide a further step to implementation, a software-in-the-loop simulation model is
developed. A fully dynamic model of the powertrain and the control algorithm are
implemented in AMESim-Simulink co-simulation environment. This shows small
deterioration of the control performance by driver’s pedal action, powertrain dynamics
and limited computational precision on the controller performance.
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Khariwal, Vivek. "Adaptive control of real-time media applications in best-effort networks." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1236.
Full textAbstract:
Quality of Service (QoS) in real-time media applications can be defined as the
ability to guarantee the delivery of packets from source to destination over best-effort
networks within some constraints. These constraints defined as the QoS metrics are
end-to-end packet delay, delay jitter, throughtput, and packet losses. Transporting
real-time media applications over best-effort networks, e.g. the Internet, is an area
of current research. Both the Transmission Control Protocol (TCP) and the User
Datagram Protocol (UDP) have failed to provide the desired QoS. This research aims
at developing application-level end-to-end QoS controls to improve the user-perceived
quality of real-time media applications over best-effort networks, such as, the public
Internet.
In this research an end-to-end packet based approach is developed. The end-to-
end packet based approach consists of source buffer, network simulator ns-2, destina-
tion buffer, and controller. Unconstrained model predictive control (MPC) methods
are implemented by the controller at the application layer. The end-to-end packet
based approach uses end-to-end network measurements and predictions as feedback
signals. Effectiveness of the developed control methods are examined using Matlab
and ns-2. The results demonstrate that sender-based control schemes utilizing UDP
at transport layer are effective in providing QoS for real-time media applications
transported over best-effort networks. Significant improvements in providing QoS are
visible by the reduction of packet losses and the elimination of disruptions during the
playback of real-time media. This is accompanied by either a decrease or increase in
the playback start-time.
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Abraham, Etimbuk. "Adaptive supervisory control scheme for voltage controlled demand response in power systems." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-supervisory-control-scheme-for-voltage-controlled-demand-response-in-power-systems(3e64537d-52c7-4eb5-87f2-b73fe920b9cb).html.
Full textAbstract:
Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.
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Shamsudin, Syariful Syafiq. "The Development of Neural Network Based System Identification and Adaptive Flight Control for an AutonomousHelicopter System." Thesis, University of Canterbury. Mechanical Engineering Department, 2013. http://hdl.handle.net/10092/8803.
Full textAbstract:
This thesis presents the development of self adaptive flight controller for an unmanned helicopter system under hovering manoeuvre. The neural network (NN) based model predictive control (MPC) approach is utilised in this work. We use this controller due to its ability to handle system constraints and the time varying nature of the helicopter dynamics. The non-linear NN based MPC controller is known to produce slow solution convergence due to high computation demand in the optimisation process. To solve this problem, the automatic flight controller system is designed using the NN based approximate predictive control (NNAPC) approach that relies on extraction of linear models from the non-linear NN model at each time step. The sequence of control input is generated using the prediction from the linearised model and the optimisation routine of MPC subject to the imposed hard constraints. In this project, the optimisation of the MPC objective criterion is implemented using simple and fast computation of the Hildreth's Quadratic Programming (QP) procedure.
The system identification of the helicopter dynamics is typically performed using the time regression network (NNARX) with the input variables. Their time lags are fed into a static feed-forward network such as the multi-layered perceptron (MLP) network. NN based modelling that uses the NNARX structure to represent a dynamical system usually requires a priori knowledge about the model order of the system. Low model order assumption generally leads to deterioration of model prediction accuracy. Furthermore, massive amount of weights in the standard NNARX model can result in an increased NN training time and limit the application of the NNARX model in a real-time application. In this thesis, three types of NN architectures are considered to represent the time regression network: the multi-layered perceptron (MLP), the hybrid multi-layered perceptron (HMLP) and the modified Elman network. The latter two architectures are introduced to improve the training time and the convergence rate of the NN model. The model structures for the proposed architecture are selected using the proposed Lipschitz coefficient and k-cross validation methods to determine the best network configuration that guarantees good generalisation performance for model prediction.
Most NN based modelling techniques attempt to model the time varying dynamics of a helicopter system using the off-line modelling approach which are incapable of representing the entire operating points of the flight envelope very well. Past research works attempt to update the NN model during flight using the mini-batch Levenberg-Marquardt (LM) training. However, due to the limited processing power available in the real-time processor, such approaches can only be employed to relatively small networks and they are limited to model uncoupled helicopter dynamics. In order to accommodate the time-varying properties of helicopter dynamics which change frequently during flight, a recursive Gauss-Newton (rGN) algorithm is developed to properly track the dynamics of the system under consideration.
It is found that the predicted response from the off-line trained neural network model is suitable for modelling the UAS helicopter dynamics correctly. The model structure of the MLP network can be identified correctly using the proposed validation methods. Further comparison with model structure selection from previous studies shows that the identified model structure using the proposed validation methods offers improvements in terms of generalisation error. Moreover, the minimum number of neurons to be included in the model can be easily determined using the proposed cross validation method. The HMLP and modified Elman networks are proposed in this work to reduce the total number of weights used in the standard MLP network. Reduction in the total number of weights in the network structure contributes significantly to the reduction in the computation time needed to train the NN model. Based on the validation test results, the model structure of the HMLP and modified Elman networks are found to be much smaller than the standard MLP network. Although the total number of weights for both of the HMLP and modified Elman networks are lower than the MLP network, the prediction performance of both of the NN models are on par with the prediction quality of the MLP network.
The identification results further indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than the off-line LM method. The rGN method is found capable of producing satisfactory prediction accuracy even though the model structure is not accurately defined. The recursive method presented here in this work is suitable to model the UAS helicopter in real time within the control sampling time and computational resource constraints. Moreover, the implementation of proposed network architectures such as the HMLP and modified Elman networks is found to improve the learning rate of NN prediction. These positive findings inspire the implementation of the real time recursive learning of NN models for the proposed MPC controller. The proposed system identification and hovering control of the unmanned helicopter system are validated in a 6 degree of freedom (DOF) safety test rig. The experimental results confirm the effectiveness and the robustness of the proposed controller under disturbances and parameter changes of the dynamic system.
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ISAIA, FRANCESCO. "Exploiting the potential of adaptive building components by means of innovative control strategies." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2841166.
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Schenck, Wolfram. "Adaptive internal models for motor control and visual prediction." Berlin Logos-Verl, 2008. http://d-nb.info/989979113/04.
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Lin, Xiaohai [Verfasser]. "Robust and Stochastic Model Predictive Control of Linear Systems with Predictable Additive Disturbance : with Application to Multi-Objective Adaptive Cruise Control / Xiaohai Lin." Düren : Shaker, 2020. http://d-nb.info/121347261X/34.
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Paula, Neander Alessandro da Silva. "MPC adaptativo - multimodelos para controle de sistemas não-lineares." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-14052009-000836/.
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Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia.During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.
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Li, Hancao. "Modeling and control of a pressure-limited respirator and lung mechanics." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47667.
Full textAbstract:
The lungs are particularly vulnerable to acute, critical illness. Respiratory failure can result not only from primary lung pathology, such as pneumonia, but also as a secondary consequence of heart failure or inflammatory illness, such as sepsis or trauma. When this occurs, it is essential to support patients with mechanical ventilation while the fundamental disease process is addressed. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation that ensures organ function. Achieving these goals is complicated by the fact that mechanical ventilation can actually cause acute lung injury, either by inflating the lungs to excessive volumes or by using excessive pressures to inflate the lungs. Thus, the challenge to mechanical ventilation is to produce the desired blood levels of carbon dioxide and oxygen without causing further acute lung injury.
In this research, we develop an analysis and control synthesis framework for a pressure-limited respirator and lung mechanics system using compartment models. Specifically, a general mathematical model is developed for the dynamic behavior of a multicompartment respiratory system. Then, based on this multicompartment model, an optimal respiratory pattern is characterized using classical calculus of variations minimization techniques for inspiratory and expiratory breathing cycles. Furthermore, model predictive controller frameworks are designed to track the given optimal respiratory air flow pattern while satisfying control input amplitude and rate constrains.
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Wirsching, Leonard [Verfasser], and Hans Georg [Akademischer Betreuer] Bock. "Multi-Level Iteration Schemes with Adaptive Level Choice for Nonlinear Model Predictive Control / Leonard Wirsching ; Betreuer: Hans Georg Bock." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177251639/34.
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Chow, Andy Ho Fai. "Adaptive traffic control system : a study of strategies, computational speed and effect of prediction error /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202002%20CHOW.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 126-129). Also available in electronic version. Access restricted to campus users.
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Tang, Meng. "The Adaptive Intelligent Model for Process Diagnosis, Prediction and Control." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for produksjons- og kvalitetsteknikk, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-328.
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This research work focuses at first on the intelligent model development for process state (special for fault) detection, behavior prediction and process control for complex industrial processes. In the model architecture, Fuzzy Neural Networks (FNNs) are employed as process state classifiers for process state (fault) detection; other (different) Neural Networks (NNs) models are applied for system identification of process characteristics in different process states. The model detects process states (faults) and predicts process behavior according to process input and historical behaviors, whose combination of influences generates the final results of process state (fault) detection and quantitative prediction. The whole model is constructed based on Fuzzy TS NARX models. Secondly, an optimal model is designed to two purposes, one is for optimal process diagnosis and another is for optimal prediction. To time varying processes, an adaptive strategy and algorithm, applying the Least Squares algorithm, has been developed for model adaptability to cover time depending process changes. Thirdly, a specific state space equation of discrete time varying system is being derived from the model. In the state space equation, the state transition matrix A is determined by the fuzzy degree of process state classification produced by process historical behavior in time t instant, and the input transition matrix B by process real input in time t instant. The state observer vector H is determined by optimization results generated by model adaptive or optimal scheme. Finally, to confirm the validity of the theoretical results from above, an application case has been studied for supply forecasting. The study and application results indicate that the model not only has good performance for fault detection, but also provides excellent quantitative prediction of process output. It can be applied in process state (fault) detection, diagnosis and prediction for process behavior, as well as fault predictive control.
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Gonçalves, Diogo Antunes. "Energy management systems based on adaptive surrogate modelling." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23559.
Full textAbstract:
Doutoramento em Sistemas Energéticos e Alterações ClimáticasEstima-se que o sector dos edifícios seja responsável por cerca de 40% da totalidade de energia consumida na União Europeia e Estados Unidos da América. 50% dessa energia está alocada a sistemas de aquecimento, ventilação e ar-condicionado (AVAC), dos quais 20% estimam-se ser desperdiçados devido a ineficiência na gestão de energia. Considera-se pertinente focar-se no melhoramento da eficiência energética do edificado, reduzindo o desperdício de forma a evitar a escassez de recursos fósseis, bem como para mitigar os problemas ambientais e as alterações climáticas causadas pelo consumo e produção de energia. A tese propõe abordagens e metodologias que permitem tomar o controlo preditivo de supervisão dos sistemas de climatização enquanto medida de reabilitação energética na requalificação de edifícios. A principal contribuição deste trabalho prende-se com a implementação e desenvolvimento de metamodelos adaptativos baseados em aprendizagem computacional que assistam o processo de otimização multi-objetivo inerente ao controlo supervisor da gestão de energia em edifícios de serviços. Esta metodologia deverá ainda permitir a sua implementação de forma agnóstica a natureza dos sistemas AVAC existentes no edifício. A metodologia apresentada propõe uma abordagem convergente com o estado da arte no desenvolvimento científico na área da inteligência artificial. O esforço mínimo requerido para a implementação deste tipo de sistema de gestão inteligente e avaliado, concluindo-se que o seu potencial de aplicação e significativo. Para este fim, foi desenvolvida uma aplicação informática capaz de conduzir toda a metodologia em regime de simulação computacional de modo a averiguar a utilidade das soluções propostas pelo sistema de controlo supervisor desenvolvido. Os resultados obtidos apresentam soluções compatíveis com o melhoramento do paradigma energético-ambiental corrente, contribuindo desse modo para uma maior sustentabilidade do edificado obsoleto em termos energéticos. Os custos com energia alocada a sistemas AVAC podem alcançar uma redução de 27% dos custos base, acompanhando uma melhoria ao nível do conforto dos ocupantes. Mesmo em casos em que a requalificação da envolvente do edifício e do sistema de climatização seja anterior a implementação de um sistema de gestão inteligente, ou que a envolvente seja já competente em termos de eficiência energética (como o caso de estudo apresentado), a poupança energética e, ainda assim, assegurada devido a natureza flexível e autodidata do sistema de supervisão proposto. Portanto, recomenda-se que a reabilitação energética de edifícios tome como prioridade a requalificação do sistema de controlo AVAC por sistemas avançados e supervisores de controlo de forma a potenciarem a inércia dos edifícios, bem como toda a informação disponível na atual era digital.
Buildings account for almost 40% of the total energy consumption in the European Union and the United States combined. From that fraction, 50% is allocated to the heating, ventilation and air-conditioning systems (HVAC), from which 20% is wasted due to system's ine ciency. Considering that most of this energy is obtained from scarce fossil reserves and its consumption has an adverse impact on the climate change problem, it is of utmost importance to reduce energy wastes, namely by improving the overall energy e ciency of buildings. This thesis postulates the implementation of intelligent supervisory control systems for new or existing HVAC equipment as an energy retro tting measure concurrent with conventional architectural and systems retro tting. The proposed methodology is characterized by a exible, yet robust predictive control algorithm, capable of supervising generic HVAC systems in real-time by suggesting high-level controls, resulting in an optimized compromise between occupants' comfort requirements and energy consumption (and/or cost), taking advantage of the building constructive characteristics and information availability. The proposed solution integrates the exibility of machine learning techniques with the robustness of surrogate models to deliver data-driven predictive models capable of assisting the multi-objective optimization problem of minimizing energy consumption and cost while improving occupants comfort. The proposed modelling and optimization strategies are presented as a novelty capable of answering the quest for a robust yet exible supervisory predictive control for generic HVAC systems. A software package capable of delivering advanced and generic supervisory predictive controls, especially focusing on the scope of building energy retro tting was developed and used as the delivery method for the results presented in this thesis. The obtained results suggest that o ce buildings, characterized by a contemporary construction and HVAC system, can be improved regarding overall energy e ciency and occupants comfort by retro tting the control solution adding a supervisory predictive control level, external to the existing HVAC system. The expected energy saving by considering the proposed control are in line with the requirements imposed by the present energy and climate change framework, with up to 27% savings of energy related costs due to autonomous demand shifting. Moreover, it is recommended that building energy retro ts should consider as a priority the update of the energy control strategies by adding supervisory solutions capable of capitalizing the use of the building thermal inertia as well as the available data in this current information era (occupancy schedules, weather, etc.).
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GODBOLE, AMIT ARUN. "ADAPTIVE IMPROVEMENT OF CLIMB PERFORMANCE." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1061303791.
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Annamalai, Andy S. K. "An adaptive autopilot design for an uninhabited surface vehicle." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3100.
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An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.
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Shui, Yuhao. "Strategic Trajectory Planning of Highway Lane Change Maneuver with Longitudinal Speed Control." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1431093441.
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BRANDI, SILVIO. "Deep Reinforcement Learning-based Control Strategies for Enhancing Energy Management in HVAC Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2971112.
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D'Angio, Paul Christopher. "Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27582.
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This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind.
The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver.
This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time.
The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driverâ s hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driverâ s seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle.Ph. D.
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Nguyen, Minh Tri. "Commande adaptative multivariable avec contraintes." Grenoble INPG, 1989. http://www.theses.fr/1989INPG0100.
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On aborde la commande predictive avec contraintes, basee sur la minimisation d'un critere quadratique a horizon fini fuyant, et la commande predictive sous contraintes avec placement de poles asymptotiques. Test du comportement de la commande predictive avec une enceinte climatique multivariable
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Åfeldt, Tom. "Adaptive Steering Behaviour for Heavy Duty Vehicles." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215134.
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Today the majority of the driver assistance systems are rule-basedcontrol systems that help the driver control the truck. But driversare looking for something more personal and exible that can controlthe truck in a human way with their own preferences. Machine learningand articial intelligence can help achieve this aim. In this studyArticial Neural Networks are used to model the driver steering behaviourin the Scania Lane Keeping Assist. Based on this, trajectoryplanning and steering wheel torque response are modelled to t thedriver preference. A model predictive controller can be used to maintainstate limitations and to weigh the two modelled driver preferencestogether. Due to the diculties in obtaining an internal plant modelfor the model predictive controller a variant of a PI-controller is addedfor integral action instead. The articial neural network also containsan online learning feature to further customize the t to the driverpreference over time.Idag används till största del regelbaserade reglersystem förförarassistanssystem i lastbilar. Men lastbilschaufförer vill ha någotmer personligt och flexibelt, som kan styra lastbilen på ett mänskligtsätt med förarens egna preferenser. Maskininlärning och artificiell intelligenskan hjälpa till för att uppnå detta mål. I denna studie användsartificiella neurala nätverk för att modellera förarens styrbeteende genomScania Lane Keeping Assist. Med användning av detta modellerasförarens preferenser med avseende på placering på vägbanan och momentpåslag på ratten. En modell prediktiv kontroller kan användas föratt begränsa tillstånd och för att väga de två modellerade preferensernamot varann. Eftersom det var mycket svårt att ta fram den internaprocessmodellen som krävdes för regulatorn används istället en variantav en PI-kontroller för att styra lastbilen. De artificiella neuralanätverken kan också tillåtas att lära sig under körning för att anpassasig till förarens preferenser över tid.
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Koessler, Adrien. "Contribution à l'agrandissement de l'espace de travail opérationnel des robots parallèles. Vérification du changement de mode d'assemblage et commande pour la traversée des singularités." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC075/document.
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En comparaison avec leurs homologues sériels, les robots parallèles possèdent de nombreux avantages : rigidité, temps de cycle et précision de positionnement. La faible taille de leur espace de travail opérationnel est cependant un inconvénient qui empêche leur développement. L’analyse cinématique décrit la division de l’espace de travail total en aspects, séparés entre eux par des singularités de Type 2. Parmi les solutions d’élargissement de l’espace de travail opérationnel issues de la littérature, que sont comparées entre elles, nous retiendrons la traversée des singularités grâce à une génération de trajectoire et une loi de commande dédiées. Cette solution est cependant sujette aux échecs de traversée et ne permet pas de certifier la réussite de l’opération. La première partie du travail consiste donc à développer un outil permettant de détecter le résultat des traversées. De tels algorithmes n’existent pas dans la littérature ; en effet, les solveurs du problème géométrique direct ne peuvent pas donner de résultats appropriés à proximité des singularités. Cependant les méthodes ensemblistes proposent une manière intéressante de tenir compte de la cinématique du robot. Nous nous basons sur cette considération pour développer un algorithme réalisant le suivi de la pose ainsi que de la vitesse de l’effecteur du robot. En simulation, nous démontrons sa capacité à détecter les changements de mode d’assemblage et son utilité pour générer des trajectoires permettant la traversée. La deuxième problématique consiste à améliorer le suivi de trajectoire par l’utilisation de techniques de commande avancée. Une revue de littérature nous permet d’identifier la commande adaptative et la commande prédictive comme deux schémas intéressants pour notre application. La synthèse d’un loi de commande adaptative par des outils linéaires est proposée et complétée par la prédiction des paramètres dynamiques suivant la méthode Predcitive Functionnal Control. Les gains apportés par les lois de commande proposées sont évalués en simulation. Afin de valider ces contributions, elles sont implémentées sur un robot parallèle plan à deux degrés de liberté nommé DexTAR. Nécessaire à la mise en oeuvre de l’algorithme de détection du mode d’assemblage, un étalonnage géométrique du robot est réalisé et les paramètres estimés sont certifiés de manière ensembliste. La capacité de l’algorithme à statuer sur le mode d’assemblage final est ensuite évaluée sur des trajectoires réelles. Ces résultats sont comparés avec ceux obtenus en simulation. De plus, les lois de commande développées sont implémentées sur le robot DexTAR et testés dans des cas de figure réalistes comme les traversées multiples ou la saisie d’objets.Les propositions formulées dans ce manuscrit permettent de répondre à ces problématiques, afin de faciliter l’utilisation des méthodes d’agrandissement de l’espace de travail par traversée de singularité de Type 2Compared to their serial counterparts, parallel robots have the edge in terms of rigidity,cycle time and positioning precision. However, the reduced size of their operationalworkspace is a drawback that limits their use in the industry. Kinematic analysis explainshow the workspace is divided in aspects, separated from each others by so-called Type 2singularities. Among existing solutions for workspace enlargement, which are evaluatedin this thesis, we chose to work on a method based on singularity crossing. This can beachieved thanks to dedicated trajectory generators and control strategies. Yet, failuresin crossing can still happen and crossing success cannot be certified.In consequence, the first part of the thesis consists in the development of an algorithmable to state on the results of a crossing attempt. Such a tool does not exist inthe literature, since solvers for the forward kinematics of parallel robots diverge aroundsingularities. Nonetheless, interval methods allow to bypass this problem by trackingend-effector velocity alongside with its pose. The ability of the algorithm to detect assemblymode change is proven in simulation, and its usefulness for reliable trajectoryplanning is shown.In a second part, we seek to improve trajectory tracking through the use of advancedcontrol techniques. A review on those techniques showed adaptive control and predictivecontrol methods to be well-fitted to our application. Linear synthesis of articularadaptive control is proposed and then derived in order to predict dynamic parametersthanks to the Predictive Functional Control method. Efficiency of the proposed controllaws is evaluated in simulation.1In order to validate both contributions, algorithms and control laws are implementedon a 2-degree of freedom planar parallel robot named DexTAR. As it is mandatory forassembly mode detection, the kinematic calibration of the robot is completed from whichcertified geometric parameters are derived. Assembly mode detection is performed onreal trajectories and results are compared to those obtained in simulation. Moreover,adaptive and predictive control laws are tested in realistic cases of singularity crossingand object manipulation.Overall, proposed contributions answer the problems that were stated previously andform an improvement to the workspace enlargement method based on Type 2 singularitycrossing
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Vale, Valentina Alessandra Carvalho do. "Controle de posição de um robô cartesiano por meio de técnicas adaptativas." Universidade Federal da Paraíba, 2011. http://tede.biblioteca.ufpb.br:8080/handle/tede/5311.
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Previous issue date: 2011-08-18Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
This paper presents a design of a predictive adaptive controller and a hybrid controller for a electro pneumatic manipulator robot with three Cartesian degrees of freedom (3 DOF). The manipulator robot is composed by three electro-pneumatic valves and pneumatic cylinders for three, two with 500mm forming the XZ axis and a 400mm on the vertical axis Y. The cylinders are driven by three electro-pneumatic proportional valves controlled by computer, which directs the flow of compressed air as the needed position. Attached to the rods of each cylinder, there are scales for potentiometric measurement of their respective positions. Through two acquisition boards, electro-pneumatic valves and potentiometric scales are connected to the computer and the data is processed using the software LabVIEW® and MATLAB®. The controllers are developed through explicit models of the electropneumatic manipulator robot estimated in real time by Recursive Least Squares Algorithm (RLS).
Neste trabalho apresentam-se projetos de um controlador adaptativo preditivo e de um híbrido para um robô manipulador eletropneumático de três graus de liberdade (3 GDL) cartesiano. O robô manipulador é composto basicamente por três válvulas eletropneumáticas e por três cilindros pneumáticos, dois de 500mm formando o plano XZ e um de 400mm no eixo vertical Y. Os cilindros são acionados através de três válvulas eletropneumáticas proporcionais comandadas por computador, que direcionam o fluxo de ar comprimido conforme a necessidade de posicionamento. Acopladas às hastes de cada cilindro, estão réguas potenciométricas para medição de suas respectivas posições. Através de duas placas de aquisição, as válvulas eletropneumáticas e as réguas potenciométricas são conectadas ao computador e os dados são processados utilizando os softwares LabVIEW® e Matlab®. Os controladores são desenvolvidos através de modelos explícitos do robô manipulador eletropneumático estimados em tempo real pelo Algoritmo dos Mínimos Quadrados Recursivo (MQR).
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Wilkerson, Jaxon. "Handoff of Advanced Driver Assistance Systems (ADAS) using a Driver-in-the-Loop Simulator and Model Predictive Control (MPC)." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595262540712316.
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Cavalcanti, Anderson Luiz de Oliveira. "Estudo e implementa??o de um controlador preditivo generalizado bilinear compensado adaptativo." Universidade Federal do Rio Grande do Norte, 2003. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15425.
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Previous issue date: 2003-12-12The present work presents the study and implementation of an adaptive bilinear compensated generalized predictive controller. This work uses conventional techniques of predictive control and includes techniques of adaptive control for better results. In order to solve control problems frequently found in the chemical industry, bilinear models are considered to represent the dynamics of the studied systems. Bilinear models are simpler than general nonlinear model, however it can to represent the intrinsic not-linearities of industrial processes. The linearization of the model, by the approach to time step quasilinear , is used to allow the application of the equations of the generalized predictive controller (GPC). Such linearization, however, generates an error of prediction, which is minimized through a compensation term. The term in study is implemented in an adaptive form, due to the nonlinear relationship between the input signal and the prediction error.Simulation results show the efficiency of adaptive predictive bilinear controller in comparison with the conventional.
O presente trabalho tem como objetivo o estudo e a implementa??o de um controlador preditivo generalizado bilinear adaptativamente compensado. Este trabalho utiliza t?cnicas de controle preditivo convencionais juntamente com t?cnicas de controle adaptativo na busca de um melhor resultado. No intuito de solucionar problemas de controle freq?entemente enfrentados pela ind?stria qu?mica, ? proposto o modelo bilinear para representar a din?mica dos sistemas em estudo. Os modelos bilineares s?o uma classe particular dentre os modelos n?o-lineares, por?m s?o por natureza mais simples que os modelos n?o lineares gerais e ainda conseguem representar as n?o-linearidades intr?nsecas dos processos industriais. A lineariza??o do modelo, pela aproxima??o quasilinear por degrau de tempo, ? utilizada para viabilizar a aplica??o das equa??es do controlador preditivo generalizado (GPC). Tal lineariza??o, no entanto, gera um erro de predi??o, o qual ? minimizado atrav?s de um termo de compensa??o. O termo em estudo ? implementado de forma adaptativa, dada a forte rela??o n?o-linear entre o sinal de entrada e o erro de predi??o. Resultados de simula??o mostram a efici?ncia do controlador preditivo bilinear adaptativo em compara??o com o convencional.