Dissertations / Theses: 'Multiple model adaptive control'

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Buchstaller, Dominic. "Robust stability and performance for multiple model switched adaptive control." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/72334/.

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While the concept of switching between multiple controllers to achieve a control objective is not new, the available analysis to date imposes various structural and analytical assumptions on the controlled plant. The analysis presented in this thesis, which is concerned with an Estimation-based Multiple Model Switched Adaptive Control (EMMSAC) algorithm originating from Fisher-Jeffes (2003), Vinnicombe (2004), is shown not to have such limitations. As the name suggests, the key difference between EMMSAC and common multiple model type switching schemes is that the switching decision is based on the outcome of an optimal estimation process. The use of such optimal estimators is the key that allows for a simplified, axiomatic approach to analysis. Also, since estimators may be implemented by standard optimisation techniques, their construction is feasible for a broad class of systems. The presented analysis is the first of its kind to provide comprehensive robustness and performance guarantees for a multiple model control algorithm, in terms of $l_p,\ 1\le p\le \infty$ bounds on the closed loop gain, and is applicable to the class of minimal MIMO LTI plants. A key feature of this bound is that it permits the on-line alteration of the plant model set (dynamic EMMSAC) in contrast to the usual assumption that the plant model set is constant (static EMMSAC). It is shown that a static EMMSAC algorithm is conservative whereas a dynamic EMMSAC algorithm, based on the technique of dynamically expanding the plant model set, can be universal. It is also shown that the established gain bounds are invariant to a refinement of the plant model set, e.g. as a successive increasing fidelity sampling of a continuum of plants. Dynamic refinement of the plant model set is considered with the view to increase expected performance. Furthermore, the established bounds --- which are also a measure of performance --- have the property that they are explicit in the free variables of the algorithm. It is shown that this property of the bound forms the basis for a principled, performance-orientated approach to design. Explicit, performance-orientated design examples are given and the trade off between dynamic and static constructions of plant model sets are investigated with respect to prior information on the acting disturbances and the uncertainty.

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Wang, Yu. "Adaptive control and learning using multiple models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783473.

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Adaptation can have different objectives. Compared to a learning behavior, which is mainly to optimize the rewards/experience obtained through the learning process, adaptive control is a type of adaptation that follows a specific target guided by a controller. Although the targets may be different, the two types of adaption share common research interests.

One of the popular research techniques for studying adaptation is the use of multiple models, where the system will utilize information from multiple environment observers instead of one to improve the adaptation behavior in terms of stability, speed and accuracy. In this thesis, applications of multiple models for two types of adaptation, adaptive control and learning, will be investigated separately. For adaptive control, the research focuses on second-level adaptation, which is a new multiple-model-based approach; for learning, the multiple model concept is designed and embedded into a type of reinforcement scheme: learning automata.

The stability, robustness and performance of second-level adaptation will be first investigated in the context of various environments, including time-varying plants and noisy disturbances. Then, a new design of second-level adaptation for general systems and input-output accessible systems will be discussed. The reasons for the improved performance using second-level adaptation are analyzed theoretically. The second part of the thesis contributes to a new method of learning automata using multiple models. The method is first applied to a two-state (binary) reward environment in the simplest case, and it is later extended to the feed-forward case when multiple states or actions are presented. Finally, general reinforcement learning automata for network cases will be discussed. In all cases, simulation studies are given, wherever appropriate, to demonstrate the improvement in performance compared to conventional approaches.

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Brend, O. "Implementation and experimental evaluation of multiple model switched adaptive control for FES-based rehabilitation." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/364612/.

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Functional electrical stimulation (FES) is a well-established approach that is employed as a therapeutic tool for the restoration of motor control in individuals experiencing muscle impairment. Although its use as a rehabilitation tool is validated by clinical results, current control approaches limit the full exploitation of its potential due to the lack of accuracy with which the FES is applied. Research has thus focused on the use of advanced, closed-loop control algorithms to provide more accurate FES that is both task-oriented, and matches the rehabilitation needs of the patient. Experimental results have been reported for a variety of control schemes. However, the majority of approaches have failed to transfer to clinical practice due to the difficulties associated with identifying a model of electrically stimulated muscle that adapts as the true plant varies with time. Estimation-based multiple model switched adaptive control (EMMSAC) is a robust control approach that has the potential to overcome the problems associated with the uncertain, time-varying properties of electrically stimulated muscle. EMMSAC utilises optimal disturbance estimation to assess the respective performances of a set of candidate plant models. Then the controller associated with the model that has best performance is switched into closed-loop operation. This thesis details the algorithmic modifications that allow disturbance estimation to be performed in the time-varying setting for nonlinear Hammerstein structures. Then it is shown experimentally that a general plant model set can be identified that represents the time-varying, FES-induced muscle activation dynamics for the population of younger healthy adults. This finding is exploited to design an EMMSAC controller that achieves accurate trajectory tracking for multiple participants with minimal prior model identification. Results indicate that the use of EMMSAC reduces RMS tracking error when compared with a fixed controller; similar results are also reported for older healthy participants. Furthermore, initial results for a small sample of stroke-participants are shown, which confirms the potential for the proposed control approach to be applied in a clinical setting for FES-based rehabilitation.

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Wang, Xiaoru. "Multi-Core Implementation of F-16 Flight Surface Control System Using GA Based Multiple Model Reference Adaptive Control Algorithm." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302130339.

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Kamalasadan, Sukumar. "A New Generation of Adaptive Control: An Intelligent Supervisory Loop Approach." University of Toledo / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1087223752.

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Sova, Václav. "Adaptivní řízení elektromechanických aktuátorů s využitím dopředného kompenzátoru založeného na více-modelovém přístupu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-391815.

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This thesis deals with the derivation of novel adaptive feed forward compensator, which will be used for the control of the electromechanical actuators used in automotive industry. The electromechnical actuators are an electronic throttle valve and an EGR valve. The introduced adaptive compensator is derived from an existing multiple model feedback control method. This work describes the derivation of this method and simulation and experimental verification. In addition, the most well known digital filter differentiators are presented and summarized in this paper because the feed forward compensator needs them for its operation. From these filters, one specific is chosen, whoose coefficients for the specific setting leads to integer multiplication and an integer implementation of the filter. This will be used to implement this filter to the FPGA and then we prove, that this implementation saves a lot of FPGA resources compared to filters implemented using fixed or floating-point arithmetic.

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Choi, Jinbae. "Closed-Loop Optimal Control of Discrete-Time Multiple Model Linear Systems with Unknown Parameters." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1441178373.

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Ni, Lingli. "Fault-Tolerant Control of Unmanned Underwater Vehicles." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28187.

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Unmanned Underwater Vehicles (UUVs) are widely used in commercial, scientific, and military missions for various purposes. What makes this technology challenging is the increasing mission duration and unknown environment. It is necessary to embed fault-tolerant control paradigms into UUVs to increase the reliability of the vehicles and enable them to execute and finalize complex missions. Specifically, fault-tolerant control (FTC) comprises fault detection, identification, and control reconfiguration for fault compensation. Literature review shows that there have been no systematic methods for fault-tolerant control of UUVs in earlier investigations. This study presents a hierarchical methodology of fault detection, identification and compensation (HFDIC) that integrates these functions systematically in different levels. The method uses adaptive finite-impulse-response (FIR) modeling and analysis in its first level to detect failure occurrences. Specifically, it incorporates a FIR filter for on-line adaptive modeling, and a least-mean-squares (LMS) algorithm to minimize the output error between the monitored system and the filter in the modeling process. By analyzing the resulting adaptive filter coefficients, we extract the information on the fault occurrence. The HFDIC also includes a two-stage design of parallel Kalman filters in levels two and three for fault identification using the multiple-model adaptive estimation (MMAE). The algorithm activates latter levels only when the failure is detected, and can return back to the monitoring loop in case of false failures. On the basis of MMAE, we use multiple sliding-mode controllers and reconfigure the control law with a probability-weighted average of all the elemental control signals, in order to compensate for the fault. We validate the HFDIC on the steering and diving subsystems of Naval Postgraduate School (NPS) UUVs for various simulated actuator and/or sensor failures, and test the hierarchical fault detection and identification (HFDI) with realistic data from at-sea experiment of the Florida Atlantic University (FAU) Autonomous Underwater Vehicles (AUVs). For both occasions, we model actuator and sensor failures as additive parameter changes in the observation matrix and the output equation, respectively. Simulation results demonstrate the ability of the HFDIC to detect failures in real time, identify failures accurately with a low computational overhead, and compensate actuator and sensor failures with control reconfiguration. In particular, verification of HFDI with FAU data confirms the performance of the fault detection and identification methodology, and provides important information on the vehicle performance.
Ph. D.

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Pinguet, Jérémy. "Contribution à la synthèse de contrôleurs neuronaux robustes par imitation." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG002.

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Cette thèse s'intéresse à l'élaboration de systèmes de contrôle par imitation de comportements ou de décisions répondant à des exigences complexes. L'objectif est de réaliser l'apprentissage d'un contrôleur neuronal de façon efficace et robuste sur une base de données regroupant ces comportements.L'approche retenue unifie les outils de la commande robuste avec ceux de la modélisation par réseaux de neurones. Des méthodes d'identification de systèmes dynamiques sont tout d'abord développées selon des structures neuronales en cohésion avec les représentations de systèmes linéaires à paramètres variants. L'accès à ce domaine d'étude ouvre alors la voie à l'analyse de stabilité et de performance de ces types de modèles neuronaux.Les travaux proposent par la suite d'exploiter ces propriétés afin de répondre aux enjeux de robustesse inhérents à l'apprentissage de lois de commande. La méthode d'identification de contrôleurs robustes qui est proposée, repose sur l'évaluation des marges de stabilité de l'asservissement neuronal. Il est alors permis de consolider la robustesse des contrôleurs à travers une stratégie d'apprentissage avec optimisation de la stabilité par une formulation multi-objectifs. En complément, le déploiement des contrôleurs est effectué selon une méthode de contrôle adaptative multi-modèle.La démarche est finalement appliquée aux pilotes automatiques d'avion via une co-simulation avec un simulateur de vol caractérisé par sa grande fiabilité de modélisation. Les problématiques de contrôle abordées sont, dans un premier temps de guider l'appareil selon un cap et une altitude donnés, tandis qu'une seconde expérimentation se concentre sur le suivi d'un chemin de vol constitué d'une série de points de passage. Les autopilotes neuronaux sont développées par l'imitation d'un autopilote existant puis par l'imitation d'un pilote
This thesis focuses on developing control systems by imitating behaviors or decisions meeting complex requirements. The objective is to perform the learning of a neural controller efficiently and robustly on a database containing these behaviors.The chosen approach unifies robust control tools with those of neural network modeling. Methods for identifying dynamic systems are first developed according to neural structures in cohesion with the representations of linear systems with varying parameters. Access to this field of study opens the way to stability and performance analysis of these neural models.The work then proposes to exploit these properties to address the robustness issues inherent to the learning of control laws. The proposed method of identifying robust controllers is based on evaluating the stability margins of the neural feedback loop. It is then possible to consolidate the robustness of the controllers through a learning strategy with stability optimization by a multi-objective formulation. In addition, the deployment of the controllers is performed using a multi-model adaptive control method.The approach is finally applied to aircraft autopilots via a co-simulation with a flight simulator characterized by its high modeling reliability. The control issues addressed are, in the first step, to guide the aircraft according to a given heading and altitude, while a second experiment focuses on following a flight path consisting of a series of waypoints. The neural autopilots are developed by imitating an existing autopilot and then by imitating a pilot

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10

Gendron, Sylvain. "Model weighting adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0007/NQ44437.pdf.

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Gendron, Sylvain. "Model weighting adaptive control." Thesis, McGill University, 1997. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=34965.

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The main exercise of this thesis is the formulation of a mathematical framework for analyzing an existing industrial adaptive control algorithm labeled Model weighting adaptive control (MWAC). The algorithm is then analyzed under this framework. The exercise is complemented by a set of algorithmic additions aimed at solving questions that so far had remained open (e.g. the treatment of undermodelling errors). Those solutions, on the other hand build on results derived from the analysis.
A key result for analyzing the algorithm is that when an external excitation is applied (in the form of a control task such as a setpoint change), the adaptive controller behaves, in a short time that follows the application of the excitation, as a linear equation whose parameters are completely known at design time. It follows that during this short period, the input signal provided to the estimation subsystem is at least partially known (except for disturbances) and that the estimation virtually takes place in open loop. Using this information and assuming boundedness of the disturbance signals, it is possible to bound the behaviour of the adaptive system at an early stage.
With the MWAC algorithm, the plant model is formed by making a weighted sum of a finite number of possible plant models. It is shown that, under adequate conditions and in a time corresponding to the apparent plant delay, the plant model will "jump" to a neighborhood of the true plant. The size of this neighborhood will depend in part on how sharply the bad models are discriminated from the good models. On the other hand, disturbances will smooth the weight map towards a uniform distribution. The sharpness or smoothness of the weight map can be measured online by computing the sum of the square root of all the weights in the set. The remarkable property of this measure is that an upper bound on the distance between the true plant and its model can be found which an affine function of the measure.
The effect of external disturbances such as measurement errors can be reduced by an external excitation of sufficient magnitude. This is not true however of disturbances caused by undermodelling errors which are almost always present to a lesser or greater degree. Two solutions are proposed to counteract this undesirable effect. The first method consists in bandpass filtering the input/output data in such a way that the frequency content of the data is consistent with data obtained from some first order plus delay (FOPD) model. The second method adjusts the sampling period online such that a compromise between satisfying the FOPD assumption and the coarseness of the control is obtained.

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Oram, Paul. "Internal model adaptive control." Thesis, University of Newcastle Upon Tyne, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440564.

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Keller, Uwe E. "Qualitative model reference adaptive control." Thesis, Heriot-Watt University, 1999. http://hdl.handle.net/10399/592.

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Rong, Q. "Multiple-model based nonlinear control." Thesis, Queen's University Belfast, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412562.

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Murphey, Todd David Burdick Joel Wakeman. "Control of multiple model systems /." Diss., Pasadena, Calif. : California Institute of Technology, 2002. http://resolver.caltech.edu/CaltechETD:etd-07312002-091923.

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Francisco-Revilla, Luis. "Multi-model adaptive spatial hypertext." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1444.

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Information delivery on the Web often relies on general purpose Web pages that require the reader to adapt to them. This limitation is addressed by approaches such as spatial hypermedia and adaptive hypermedia. Spatial hypermedia augments the representation power of hypermedia and adaptive hypermedia explores the automatic modification of the presentation according to user needs. This dissertation merges these two approaches, combining the augmented expressiveness of spatial hypermedia with the flexibility of adaptive hypermedia. This dissertation presents the Multi-model Adaptive Spatial Hypermedia framework (MASH). This framework provides the theoretical grounding for the augmentation of spatial hypermedia with dynamic and adaptive functionality and, based on their functionality, classifies systems as generative, interactive, dynamic or adaptive spatial hypermedia. Regarding adaptive hypermedia, MASH proposes the use of multiple independent models that guide the adaptation of the presentation in response to multiple relevant factors. The framework is composed of four parts: a general system architecture, a definition of the fundamental concepts in spatial hypermedia, an ontological classification of the adaptation strategies, and the philosophy of conflict management that addresses the issue of multiple independent models providing contradicting adaptation suggestions. From a practical perspective, this dissertation produced WARP, the first MASH-based system. WARPs novel features include spatial transclusion links as an alternative to navigational linking, behaviors supporting dynamic spatial hypermedia, and personal annotations to spatial hypermedia. WARP validates the feasibility of the multi-model adaptive spatial hypermedia and allows the exploration of other approaches such as Web-based spatial hypermedia, distributed spatial hypermedia, and interoperability issues between spatial hypermedia systems. In order to validate the approach, a user study comparing non-adaptive to adaptive spatial hypertext was conducted. The study included novice and advanced users and produced qualitative and quantitative results. Qualitative results revealed the emergence of reading behaviors intrinsic to spatial hypermedia. Users moved and modified the objects in order to compare and group objects and to keep track of what had been read. Quantitative results confirmed the benefits of adaptation and indicated a possible synergy between adaptation and expertise. In addition, the study created the largest spatial hypertext to date in terms of textual content.

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Tu, Yifeng. "Multiple Reference Active Noise Control." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36790.

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The major application of active noise control (ANC) has been focused on using a single reference signal; the work on multiple reference ANC is very scarce. Here, the behavior of multiple reference ANC is analyzed in both the frequency and time domain, and the coherence functions are provided to evaluate the effectiveness of multiple reference ANC. When there are multiple noise sources, multiple reference sensors are needed to generate complete reference signals. A simplified method combines those signals from multiple reference sensors into a single reference signal. Although this method could result in satisfactory noise control effects under special circumstances, the performance is generally compromised. A widely adopted method feeds each reference signal into a different control filter. This approach suffers from the problem of ill-conditioning when the reference signals are correlated. The problem of ill-conditioning results in slow convergence rate and high sensitivity to measurement error especially when the FXLMS algorithm is applied. To handle this particular problem, the decorrelated Filtered-X LMS (DFXLMS) algorithm is developed and studied in this thesis. Both simulations and experiments have been conducted to verify the DFXLMS algorithm and other issues associated with multiple reference ANC. The results presented herein are consistent with the theoretical analysis, and favorably indicate that the DFXLMS algorithm is effective in improving the convergence speed. To take the maximum advantage of the TMS320C30 DSP board used to implement the controller, several DSP programming issues are discussed, and assembly routines are given in the appendix. Furthermore, a graphical user interface (GUI) running under Windows' environment is introduced. The main purpose of the GUI is to facilitate parameters modification, real time data monitoring and DSP process control.
Master of Science

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Almutairi, Fawaz. "Eco-cooperative adaptive cruise control at multiple signalized intersections." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/84351.

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Consecutive traffic signals produce vehicle stops and acceleration/deceleration maneuvers on arterial roads, which may increase vehicle fuel consumption levels significantly. Eco-cooperative adaptive cruise control (Eco-CACC) systems can improve vehicle energy efficiency using connected vehicle (CV) technology. In this thesis, an Eco-CACC system is proposed to compute a fuel-optimized vehicle trajectory while traversing multiple signalized intersections. The proposed system utilizes signal phasing and timing (SPaT) information together with real-time vehicle dynamics data to compute the optimal acceleration/deceleration levels and cruise speeds for connected-technology-equipped vehicles while approaching and leaving signalized intersections, while considering vehicle queues upstream of the intersections. The INTEGRATION microscopic traffic simulation software was used to conduct a comprehensive sensitivity analysis of a set of variables, including different levels of CV market penetration rates (MPRs), demand levels, phase splits, offsets, and distances between intersections to assess the benefits of the proposed algorithm. Based on the analysis, fuel consumption saving increase with an increase in MPRs and a decrease in the cycle length. At a 100% equipped-vehicle MPR, the fuel consumption is reduced by as much as 13.8% relative to the base no Eco-CACC control. The results demonstrate an existence of optimal values for demand levels and the distance between intersections to reach the maximum fuel consumption reduction. Moreover, if the offset is near the optimal values for that specific approach, the benefits from the algorithm are reduced. The algorithm is limited to under-saturated conditions, so the algorithm should be enhanced to deal with over-saturated conditions.
Master of Science

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Harvey, Seth A. "Spacecraft attitude control using direct model reference adaptive control." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1594485351&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Wang, Xudong. "Vehicle health monitoring system using multiple-model adaptive estimation." Thesis, University of Hawaii at Manoa, 2003. http://hdl.handle.net/10125/7051.

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In this thesis, we propose two failure detection and identification (FDI) approaches based on the multiple-model estimation algorithm to monitor the health of vehicles, specifically aircraft applications. They detect and identify failing components of the vehicle, and the system variations. The dynamics of the vehicle are modeled as a stochastic hybrid system with uncertainty-unknown model structure or parameters. FDI performance is evaluated for each approach. We demonstrate the reliability, validity of these approaches by applying them to simulate aircraft machinery experiencing component failures or structural variations. The approaches that we surveyed are: (i) Multiple-Hypothesis Kalman Filter, and (ii) Interacting Multiple-Model (IMM) Estimator. By coupling the fault detection and identification (FDI) scheme with the reconfigurable controller design scheme, a fault-tolerant control system based on the multiple-model estimation algorithm is defined.
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Almutairi, Abdulgader. "Context-aware and adaptive usage control model." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9592.

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Information protection is a key issue for the acceptance and adoption of pervasive computing systems where various portable devices such as smart phones, Personal Digital Assistants (PDAs) and laptop computers are being used to share information and to access digital resources via wireless connection to the Internet. Because these are resources constrained devices and highly mobile, changes in the environmental context or device context can affect the security of the system a great deal. A proper security mechanism must be put in place which is able to cope with changing environmental and system context. Usage CONtrol (UCON) model is the latest major enhancement of the traditional access control models which enables mutability of subject and object attributes, and continuity of control on usage of resources. In UCON, access permission decision is based on three factors: authorisations, obligations and conditions. While authorisations and obligations are requirements that must be fulfilled by the subject and the object, conditions are subject and object independent requirements that must be satisfied by the environment. As a consequence, access permission may be revoked (and the access stopped) as a result of changes in the environment regardless of whether the authorisations and obligations requirements are met. This constitutes a major shortcoming of the UCON model in pervasive computing systems which constantly strive to adapt to environmental changes so as to minimise disruptions to the user. We propose a Context-Aware and Adaptive Usage Control (CA-UCON) model which extends the traditional UCON model to enable adaptation to environmental changes in the aim of preserving continuity of access. Indeed, when the authorisation and obligations requirements are fulfilled by the subject and object, and the conditions requirements fail due to changes in the environmental or the system context, our proposed model CA-UCON triggers specific actions in order to adapt to the new situation, so as to ensure continuity of usage. We then propose an architecture of CA-UCON model, presenting its various components. In this model, we integrated the adaptation decision with usage decision architecture, the comprehensive definition of each components and reveals the functions performed by each components in the architecture are presented. We also propose a novel computational model of our CA-UCON architecture. This model is formally specified as a finite state machine. It demonstrates how the access request of the subject is handled in CA-UCON model, including detail with regards to revoking of access and actions undertaken due to context changes. The extension of the original UCON architecture can be understood from this model. The formal specification of the CA-UCON is presented utilising the Calculus of Context-aware Ambients (CCA). This mathematical notation is considered suitable for modelling mobile and context-aware systems and has been preferred over alternatives for the following reasons: (i) Mobility and Context awareness are primitive constructs in CCA; (ii) A system's properties can be formally analysed; (iii) Most importantly, CCA specifications are executable allowing early validation of system properties and accelerated development of prototypes. For evaluation of CA-UCON model, a real-world case study of a ubiquitous learning (u-learning) system is selected. We propose a CA-UCON model for the u-learning system. This model is then formalised in CCA and the resultant specification is executed and analysed using an execution environment of CCA. Finally, we investigate the enforcement approaches for CA-UCON model. We present the CA-UCON reference monitor architecture with its components. We then proceed to demonstrate three types of enforcement architectures of the CA-UCON model: centralised architecture, distributed architecture and hybrid architecture. These are discussed in detail, including the analysis of their merits and drawbacks.

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MANICKAM, NITHYA. "NONLINEAR AND ADAPTIVE CONTROL OF MODEL HELICOPTER." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1144639875.

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Vaudrey, Michael A. "A novel approach to multiple reference frequency domain adaptive control." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-08292008-063731/.

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Randall, A. "Adaptive model based control for steel rolling systems." Thesis, Coventry University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364162.

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Cengiz, Orcun. "Adaptive, tactical mesh networking control base MANET model." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5122.

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Approved for public release; distribution is unlimited
Mobile Ad Hoc Networks (MANET) do not depend on any kind of established infrastructure, therefore, they can be deployed without any need of fixed infrastructure. MANET are expected to play an important role in delivering real-time services to war fighters in tactical military networks by providing infrastructureless communication. The nature of MANET, such as node mobility, unreliable transmission medium and restricted battery power, makes it more challenging for them to deliver the information warfighters need on tactical missions. As the demand for higher bandwidth real-time tactical services increases, more bandwidth efficient tactical network solutions must be developed. The goal of the CBMANET program was to develop an adaptive networking capability that dramatically improved performance and reduced communication failures in complex communication networks. However, field experiments showed that the proposed network coding for CBMANET was not adequate to leverage the limited network resources to transport timecritical messages and interactive video in varying network conditions. Therefore, CBMANET was evaluated as not usable in supporting the tactical network operations in future IT mobile services with its current coding, but it still can be useful in mobile networks that are not transferring time critical information. CBMANET remains a promising technology in the area of MANET improvements.

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Hemerly, Elder Moreira. "Model structure estimation in identification and adaptive control." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47472.

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Kress, Reid Leonard. "Adaptive model-following control for hyperthermia treatment systems." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184430.

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The purpose of this research was to develop three real-time adaptive temperature controllers for hyperthermia heating systems. Each scheme is made adaptive by using a transient Gaussian estimation routine to estimate the tissue blood perfusion and by then using these estimated values either in an optimizing routine, or in an observer, or in both. The optimizing routine uses a steady-state Gaussian estimation technique to optimize the power distribution until the best possible match is obtained between the steady-state temperatures predicted by a treatment model and a prespecified ideal temperature distribution. The observer uses a treatment model to control unmeasured locations. The first adaptive control scheme uses the optimizing routine alone, the second uses the observer alone and the third uses both the optimzing routine and observer. The performance of each of the adaptive control schemes is compared to a standard proportional-integral-derivative (PID) control scheme for one-dimensional simulations of typical treatments. Results comparing the deviation of the controlled temperature distribution to the ideal desired temperature distribution for all locations and all times indicate that the adaptive schemes perform better than the PID scheme. It can be concluded that adaptive control yields improved performance if good a priori knowledge of the treated region tissue and perfusion region boundaries is available. While these control schemes were designed for eventual implementation on a scanned focused ultrasound hyperthermia treatment system, the techniques are applicable to any system with the capability to vary specific power with respect to location and with an unknown distributed energy sink proportional to the temperature elevation.

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Hill, Jonathan. "A design procedure for model reference adaptive control." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-172226/.

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Sun, Xi. "An impedance model approach for adaptive cruise control." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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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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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, 2019
Cataloged 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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Allen, Randal T. "Robust estimation and adaptive guidance for multiple UAVs' cooperation." Orlando, Fla. : University of Central Florida, 2009. http://purl.fcla.edu/fcla/etd/CFE0002535.

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Schön, Tomas. "Identification for Predictive Control : A Multiple Model Approach." Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1050.

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Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.

This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.

The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.

Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

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Pichette, Alexandre. "Multiple model estimation and detection for adaptive guidance of hybrid systems." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80134.

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The interception of a tactical ballistic missile (TBM) represents a great challenge to the guidance and control community as the maneuver potential of the TBM is almost in the same order as the interceptor missile. The maneuver advantage of the interceptor over the TBM being reduced, a new approach for estimation and guidance for the interception of a maneuvering TBM is presented in this research. The target is assumed to use its maneuvering potential by performing a single evasive bang-bang maneuver. A simple detection algorithm for an evasive maneuver of the target combined with the use of a multiple model estimators like the interactive multiple models (IMM) led to a new adaptive guidance law, the DGL/Adapt law. Simulations results using this approach gave a reduction in the miss distance in comparison with a static guidance law.

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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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Almeida, Fernando Gomes de. "Model reference adaptive control of two axes hydraulic manipulator." Thesis, University of Bath, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334573.

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COSTA, PAULO WERNECK DE ANDRADE. "ADAPTIVE CONTROL OF A MACROECONOMETRIC MODEL WITH MEASUREMENT ERROR." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9400@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O Planejamento econômico, abordado como um problema de controle, tem por objetivo estabelecer trajetórias ótimas (ou sub-ótimas) para as variáveis que estão sujeitas ao controle do Governo. Isto significa dizer que as varáveis de política (controle) não mais serão arbitrariamente determinadas pelos seus planejadores, sendo agora resultantes de um processo de otimização , tendo em vista o cumprimento de metas previamente estabelecidas. Neste artigo aplicamos um controlador adaptativo de certeza equivalente a um modelo macroeconométrico da economia brasileira, considerando erro de medida nas variáveis de estado. A adoção de um controlador adaptativo é justificada tendo em vista as críticas (principalmente a crítica de Lucas) que recaíram sobre os modelos macroeconométricos estacionários. Uma das formas adequadas de se tratar a não estacionariedade de tais modelos é por intermédio de um controlador adaptativo cujo objetivo será controlar e identificar simultaneamente o modelo em questão. Apresentamos uma pequena resenha das aplicações de controle ótimo e controle adaptativo em problema econômicos, ressaltando a aplicação de ambas as técnicas em modelos macroeconométricos com expectativas racionais. Por intermédio de simulações comparamos a política realmente efetivada pelo governo federal e a política ótima obtida via controle ótimo não adaptativo.
Economic planning, when considered as a control problem, has as its objective establishing optimal (or sub-optimal) trajectories for the variables subject to Government Control. This means that the policy variables (control), instead of being arbitrarily determined by the policymakers, will be the result of an optimization process, with the objective of reaching pre-established goals. In this work a Certainly Equivalence Adaptative Control is applied to a macroeconometric model of the Brazilian economy with measurement error. Since the employment of time-invariant models has been widely criticized (Lucas critique) the model used here is time- varying. An adequate way to treat such a case is through an adaptative control scheme, in which control and identification of the model are perfomed simultaneously. By means of simulations the policy obtained with the adaptative controller is compared to the policy adopted by the Brazilian Government.

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Darabi, Sahneh Faryad. "Non-model based adaptive control of renewable energy systems." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/7044.

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Master of Science
Department of Mechanical and Nuclear Engineering
Guoqiang Hu
In some types of renewable energy systems such as wind turbines or solar power plants, the optimal operating conditions are influenced by the intermittent nature of these energies. This fact, along with the modeling difficulties of such systems, provides incentive to look for non-model based adaptive techniques to address the maximum power point tracking (MPPT) problem. In this thesis, a novel extremum seeking algorithm is proposed for systems where the optimal point and the optimal value of the cost function are allowed to be time varying. A sinusoidal perturbation based technique is used to estimate the gradient of the cost function. Afterwards, a robust optimization method is developed to drive the system to its optimal point. Since this method does not require any knowledge about the dynamic system or the structure of the input-to-output mapping, it is considered to be a non-model based adaptive technique. The proposed method is then employed for maximizing the energy capture from the wind in a variable speed wind turbine. It is shown that without any measurements of wind velocity or power, the proposed method can drive the wind turbine to the optimal operating point. The generated power is observed to be very close to the maximum possible values.

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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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Souissi, Slim. "Adaptive error control through packet combining in code division multiple access systems." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13381.

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Keen, Steven Dale. "Modeling driver steering behaviour using multiple-model predictive control." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611428.

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Fisher-Jeffes, Timothy Perrin. "Multiple-model switching control to achieve asymptotic robust performance." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615034.

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Gandana, Danny M. "Design and implementation of model-reference neural control systems." Thesis, University of Salford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308113.

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Messer, Richard Scott. "Analytical and experimental study of control effort associated with model reference adaptive control." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-165637/.

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Wongsavengwate, Pisamai. "Adaptive dispatching using genetic algorithms for multiple resources." Ohio : Ohio University, 1997. http://www.ohiolink.edu/etd/view.cgi?ohiou1184598551.

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Tiryaki, Kutluay Kadriye. "Adaptive Control Of Guided Missiles." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613083/index.pdf.

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iv ABSTRACT ADAPTIVE CONTROL OF GUIDED MISSILES Tiryaki Kutluay, Kadriye Ph.D., Department of Aerospace Engineering Supervisor: Asst. Prof. Dr. Ilkay Yavrucuk February 2011, 147 Pages This thesis presents applications and an analysis of various adaptive control augmentation schemes to various baseline flight control systems of an air to ground guided missile. The missile model used in this research has aerodynamic control surfaces on its tail section. The missile is desired to make skid to turn maneuvers by following acceleration commands in the pitch and yaw axis, and by keeping zero roll attitude. First, a linear quadratic regulator baseline autopilot is designed for the control of the missile acceleration in pitch axis at a single point in the flight envelope. This baseline autopilot is then augmented with a Direct Model Reference Adaptive Control (DMRAC) scheme using Neural Networks for parameter estimation, and an L1 Adaptive Control scheme. Using the linearized longitudinal model of the missile at the design point, simulations are performed to analyze and demonstrate the performance of the two adaptive augmentation schemes. By injecting uncertainties to the plant model, the effects of adaptive augmentations on the linear baseline autopilot are examined. v Secondly, a high fidelity simulation software of the missile is used in order to analyze the performance of the adaptive augmentations in 6 DoF nonlinear flight simulations. For the control of the missile in three axis, baseline autopilots are designed using dynamic inversion at a single point in the flight envelope. A linearizing transformation is employed during the inversion process. These coarsely designed baseline autopilots are augmented with L1 adaptive control elements. The performance of the adaptive control augmentation system is tested in the presence of perturbations in the aerodynamic model and increase in input gain, and the simulation results are presented.

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Fabbiane, Nicolò. "Adaptive and model-based control in laminar boundary-layer flows." Licentiate thesis, KTH, Mekanik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154052.

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In boundary-layer flows it is possible to reduce the friction drag by breaking the path from laminar to turbulent state. In low turbulence environments, the laminar-to-turbulent transition is dominated by local flow instabilities – Tollmien-Schlichting (TS) waves – that exponentially grows while being con- vected by the flow and, eventually, lead to transition. Hence, by attenuating these disturbances via localised forcing in the flow it is possible to delay farther downstream the onset of turbulence and reduce the friction drag. Reactive control techniques are widely investigated to this end. The aim of this work is to compare model-based and adaptive control techniques and show how the adaptivity is crucial to control TS-waves in real applications. The control design consists in (i) choosing sensors and actuators and (ii) designing the system responsible to process on-line the measurement signals in order to compute an appropriate forcing by the actuators. This system, called compen- sator, can be static or adaptive, depending on the possibility of self-adjusting its response to unmodelled flow dynamics. A Linear Quadratic Gaussian (LQG) regulator is chosen as representative of static controllers. Direct numerical simulations of the flow are performed to provide a model for the compensator design and test its performance. An adaptive Filtered-X Least-Mean-Squares (FXLMS) compensator is also designed for the same flow case and its per- formance is compared to the model-based compensator via simulations and experiments. Although the LQG regulator behaves better at design conditions, it lacks robustness to small flow variations. On the other hand, the FXLMS compensator proved to be able to adapt its response to overcome the varied conditions and perform an adequate control action. It is thus found that an adaptive control technique is more suitable to delay the laminar-to-turbulent transition in situations where an accurate model of the flow is not available.
I det tunna gränsskikt som uppstår en yta, kan friktionen minskas genom att förhindra omslag från ett laminärt till ett turbulent flöde. När turbulensnivån är låg i omgivningen, domineras till en början omslaget av lokala instabiliteter (Tollmien-Schlichting (TS) v ågor) som växer i en exponentiell takt samtidigt som de propagerar nedströms. Därför, kan man förskjuta omslaget genom att dämpa TS vågors tillväxt i ett gränsskikt och därmed minska friktionen.Med detta mål i sikte, tillämpas och jämförs två reglertekniska metoder, nämligen en adaptiv signalbaserad metod och en statiskt modellbaserad metod. Vi visar att adaptivitet är av avgörande betydelse för att kunna dämpa TS vågor i en verklig miljö. Den reglertekniska konstruktionen består av val av givare och aktuatorer samt att bestämma det system som behandlar mätsignaler (on- line) för beräkning av en lämplig signal till aktuatorer. Detta system, som kallas för en kompensator, kan vara antingen statisk eller adaptiv, beroende på om det har möjlighet till att anpassa sig till omgivningen. En så kallad linjär regulator (LQG), som representerar den statiska kompensator, har tagits fram med hjälp av numeriska simuleringar of strömningsfältet. Denna kompensator jämförs med en adaptiv regulator som kallas för Filtered-X Least-Mean-Squares (FXLMS) både experimentellt och numeriskt. Det visar sig att LQG regulatorn har en bättre prestanda än FXLMS för de parametrar som den var framtagen för, men brister i robusthet. FXLMS å andra sidan, anpassar sig till icke- modellerade störningar och variationer, och kan därmed hålla en god och jämn prestanda.Man kan därmed dra slutsaten att adaptiva regulatorer är mer lämpliga för att förhala omslaget fr ån laminär till turbulent strömning i situationer då en exakt modell av fysiken saknas.

QC 20141020

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48

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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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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Voss, Henry Herbert. "Model reference adaptive control of a manipulator in Cartesian coordinates." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26340.

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Robot performance is limited due to the form of the joint controllers. A model reference adaptive control scheme is proposed in which the reference model is in Cartesian coordinates. The form of the conventional joint controller is retained but the gains are adjusted to make the response similar to the reference model. By use of computer simulation the response of a manipulator to setpoint changes is examined for both the conventional and proposed control schemes. Improvements in path error for a straight line trajectory between starting and ending positions in world coordinates is observed with the new scheme compared with conventional control. The benefit is that fewer setpoints must be specified along a desired path to attain the desired path accuracy and, because of the form of the controller, servo errors will be higher leading to faster performance. Stability of the new scheme is examined by state space Floquet methods and a logical proof based on the adaption method used is given.
Applied Science, Faculty of
Mechanical Engineering, Department of
Graduate

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Dissertations / Theses on the topic 'Multiple model adaptive control'

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