Academic literature on the topic 'Offset-free model predictive control'
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Journal articles on the topic "Offset-free model predictive control"
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Pannocchia, Gabriele. "ROBUST OFFSET-FREE MODEL PREDICTIVE CONTROL." IFAC Proceedings Volumes 35, no. 1 (2002): 297–302. http://dx.doi.org/10.3182/20020721-6-es-1901.00618.
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Maeder, Urban, Francesco Borrelli, and Manfred Morari. "Linear offset-free Model Predictive Control." Automatica 45, no. 10 (October 2009): 2214–22. http://dx.doi.org/10.1016/j.automatica.2009.06.005.
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Morari, M., and U. Maeder. "Nonlinear offset-free model predictive control." Automatica 48, no. 9 (September 2012): 2059–67. http://dx.doi.org/10.1016/j.automatica.2012.06.038.
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Pannocchia, Gabriele, and James B. Rawlings. "Disturbance models for offset-free model-predictive control." AIChE Journal 49, no. 2 (February 2003): 426–37. http://dx.doi.org/10.1002/aic.690490213.
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Belda, Květoslav. "Model Predictive Control for Offset-Free Reference Tracking." TRANSACTIONS ON ELECTRICAL ENGINEERING 5, no. 1 (March 30, 2020): 8–13. http://dx.doi.org/10.14311/tee.2016.1.008.
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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">The paper deals with the offset-free reference tracking problem of the Model Predictive Control (MPC). That problem is considered for a class of the constant or occasionally changed constant reference signals. Proposed solution arises from a simple subtraction of the ARX model <br /> of two consecutive time steps. The solution is adapted <br /> to a state-space form and it corresponds to usual predictive control design without increase of the design complexity. The construction of the prediction equations and predictive controller structure is explained in the paper.</span>
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Maeder, Urban, and Manfred Morari. "Offset-free reference tracking with model predictive control." Automatica 46, no. 9 (September 2010): 1469–76. http://dx.doi.org/10.1016/j.automatica.2010.05.023.
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Ooi, W. X., A. W. Hermansson, and C. H. Lim. "Model Predictive Control – Sliding Mode Control of a pH system." IOP Conference Series: Materials Science and Engineering 1257, no. 1 (October 1, 2022): 012036. http://dx.doi.org/10.1088/1757-899x/1257/1/012036.
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Abstract This paper studies the feasibility of using discrete sliding mode controller (SMC) to achieve offset-free control of nonlinear processes in the presence of disturbances. The performance of the SMC is compared to a multiple model predictive controller (MMPC) studying the ability of set-point tracking using the pH system as a case study. The results presented from the comparison show that SMC can perform offset-free control of a pH system, with the major drawback being slow response as well as oscillation at some pH values. Finally, a design of a combination between MMPC and SMC (MMPC-SMC) is proposed, with MMPC carrying out basic control response while the SMC fulfils the role of eliminating the offset. However, the inaccurate reading in the MATLAB simulation model does not generate the expected results on the pH control. Therefore, the modification on the MATLAB models is required to achieve the improved control system on the offset-free behaviour for the set-point tracking.
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Tatjewski, Piotr. "Offset-free nonlinear Model Predictive Control with state-space process models." Archives of Control Sciences 27, no. 4 (December 1, 2017): 595–615. http://dx.doi.org/10.1515/acsc-2017-0035.
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AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.
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Wallace, Matt, Prashant Mhaskar, John House, and Timothy I. Salsbury. "Offset-Free Model Predictive Control of a Heat Pump." Industrial & Engineering Chemistry Research 54, no. 3 (January 20, 2015): 994–1005. http://dx.doi.org/10.1021/ie5017915.
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Wallace, Matt, Steven Spielberg Pon Kumar, and Prashant Mhaskar. "Offset-Free Model Predictive Control with Explicit Performance Specification." Industrial & Engineering Chemistry Research 55, no. 4 (January 20, 2016): 995–1003. http://dx.doi.org/10.1021/acs.iecr.5b03772.
Full textDissertations / Theses on the topic "Offset-free model predictive control"
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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.
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Although nonlinear model predictive control (NMPC) might be the best choice for a
nonlinear plant, it is still not widely used. This is mainly due to the computational
burden associated with solving online a set of nonlinear differential equations and a
nonlinear dynamic optimization problem in real time. This thesis is concerned with
strategies aimed at reducing the computational burden involved in different stages
of the NMPC such as optimization problem, state estimation, and nonlinear model
identification.
A major part of the computational burden comes from function and derivative evaluations
required in different parts of the NMPC algorithm. In this work, the problem is
tackled using a recently introduced efficient tool, the automatic differentiation (AD).
Using the AD tool, a function is evaluated together with all its partial derivative from
the code defining the function with machine accuracy.
A new NMPC algorithm based on nonlinear least square optimization is proposed.
In a first–order method, the sensitivity equations are integrated using a linear formula
while the AD tool is applied to get their values accurately. For higher order
approximations, more terms of the Taylor expansion are used in the integration for
which the AD is effectively used. As a result, the gradient of the cost function against
control moves is accurately obtained so that the online nonlinear optimization can be
efficiently solved.
In many real control cases, the states are not measured and have to be estimated for
each instance when a solution of the model equations is needed. A nonlinear extended
version of the Kalman filter (EKF) is added to the NMPC algorithm for this purpose.
The AD tool is used to calculate the required derivatives in the local linearization
step of the filter automatically and accurately.
Offset is another problem faced in NMPC. A new nonlinear integration is devised
for this case to eliminate the offset from the output response. In this method, an integrated disturbance model is added to the process model input or output to correct
the plant/model mismatch. The time response of the controller is also improved as a
by–product.
The proposed NMPC algorithm has been applied to an evaporation process and a
two continuous stirred tank reactor (two–CSTR) process with satisfactory results to
cope with large setpoint changes, unmeasured severe disturbances, and process/model
mismatches.
When the process equations are not known (black–box) or when these are too complicated
to be used in the controller, modelling is needed to create an internal model for
the controller. In this thesis, a continuous time recurrent neural network (CTRNN)
in a state–space form is developed to be used in NMPC context. An efficient training
algorithm for the proposed network is developed using AD tool. By automatically
generating Taylor coefficients, the algorithm not only solves the differentiation equations
of the network but also produces the sensitivity for the training problem. The
same approach is also used to solve online the optimization problem of the NMPC.
The proposed CTRNN and the predictive controller were tested on an evaporator
and two–CSTR case studies. A comparison with other approaches shows that the
new algorithm can considerably reduce network training time and improve solution
accuracy.
For a third case study, the ALSTOM gasifier, a NMPC via linearization algorithm is
implemented to control the system. In this work a nonlinear state–space class Wiener
model is used to identify the black–box model of the gasifier. A linear model of the
plant at zero–load is adopted as a base model for prediction. Then, a feedforward
neural network is created as the static gain for a particular output channel, fuel gas
pressure, to compensate its strong nonlinear behavior observed in open–loop simulations.
By linearizing the neural network at each sampling time, the static nonlinear
gain provides certain adaptation to the linear base model. The AD tool is used here
to linearize the neural network efficiently. Noticeable performance improvement is
observed when compared with pure linear MPC. The controller was able to pass all
tests specified in the benchmark problem at all load conditions.
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Hayakawa, Yoshikazu, and Tomohiko Jimbo. "Model Predictive Control for Automotive Engine Torque Considering Internal Exhaust Gas Recirculation." International Federation of Automatic Control (IFAC), 2011. http://hdl.handle.net/2237/20769.
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Da, Rù Davide. "Innovative Predictive Current Control for Synchronous Reluctance Machines." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426680.
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In recent decades, the use of power converters has become very popular in the field of electric drives. Several control techniques have been proposed for power converters and every year, the ongoing research and the always more powerful microprocessors, lead to new high performance solutions. Despite this, since the output of the worldwide research often results in complex and hardly applicable solutions, other well-established techniques, such as linear and hysteresis control with pulsewidth modulation, are still the main choice in a great number of industrial applications. The reasons of their leadership can be found considering the characteristics of these methods: on one side simplicity of comprehension and implementation and, on the other, sufficiently good performance and robustness. Due to these relevant features, despite there is still extensive room for improvements, it is not painless to propose solutions that can be attractive for people working in industry to compete with, and possibly to replace, traditional methods.
Desirably, a control algorithm for electric drives has to be simple and easily understandable. Besides, it has to be suitable for real-time applications. Robustness and reliability, beyond that performance, have to be guaranteed since the nature of the different applications, e.g. home appliances and automotive.
In this perspective, Predictive Control could represent a candidate to introduce improvements and gains in the aforementioned industrial applications. Predictive control is a wide class of controllers that uses the model of the system for the prediction of the future behavior of the controlled variables. This information is used by the regulator in order to obtain the optimal actuation, according to a predefined optimization criterion represented by a cost function. This control techique is based on concepts that are extremely simple and intuitive and besides, depending on the type of predictive control, the implementation can also be simple. In parcticular, Finite Control Set allows considering the discrete nature of the power converter and results in an extremely simple implementation. Beyond simplicity, other advantages can be recognized. First, with predictive control it is possible to avoid the cascaded structure obtaining a very fast transient response. Besides, nonlinearities can be included in the model avoiding the need of linearizing the model for a given operating point and improving the operation of the system for all conditions. Finally, it is possible to include limitations of the variables when designing the regulator.
The aim of this thesis is to study Predictive Control applied to the current control of synchronous reluctance machines, analysing and addressing some open research topics regarding this kind of control. In particular, two main aspects are studied, namely the need of a precise knowledge of the machine model and the possibility to drive a synchronous reluctance machine along the Maximum Torque per Ampere, the Flux Weakening and the Maximum Torque per Voltage operations.
The performance are strictly related to the accuracy of the model used for the prediction. In case of parameters mismatch or variation, rather than other model inadequacies, the prediction could be affected causing a worsening of the overall behaviour of the drive. The first part of this work is commited to study this aspect, analysing the effects of mismatches and variations focusing in particular on the detrimental effects of iron saturation. A novel model-free solution is presented to overcome the limitations given by an inadequate model. This method allows achieving good reference tracking and limited current ripple in every working condition. Besides, it presents great advantages in terms of simplicity: no additional hardware and no complicated calculations are required. The design is effortless since there are no gains, thresholds and so on, that have to be tuned. This technique could be used to develop an universal drive, meaning that completely different machines could be controlled with exactely the same algorithm, without self commissioning or identification procedures. Thanks to the aforementioned features, this techique could allow the spread of predictive control in industrial applications.
In order to fully exploit the characteristics of the drive while assuring the lowest power losses in every working condition, a proper control algorithm has to be used. In the second part of this work, a predictive regulator able to track the most suitable trajectory depending on the machine operation is presented. In particular, the Maximum Torque per Ampere, the Flux Weakening and the Maximum Torque per Voltage trajectories are considered. The proposal is a combination of predictive control and hysteresis control, since its aim is to keep the current error within a certain hysteresis band, and it allows combining the benefits of the two control techniques.
This study is carried out considering Predictive Current Control for Synchronous Reluctance machines. This kind of machine has been considered since it is of great interest due to the fact that it features high power density, superior reliability, high efficiency and it is cost effective due to the absence of permanent magnets and circuits in the rotor. Besides, since its significant iron saturation, its control represents a challenge (in particular) for predictive control schemes and for this reason it is a perfect case study.Negli ultimi anni, l'utilizzo di convertitori di potenza in applicazioni di azionamenti elettrici è diventato molto diffuso. Diverse tecniche di controllo per convertitori di potenza sono state proposte e ogni anno, i risultati della ricerca e gli sviluppi di microprocessori consentono di raggiungere performance sempre maggiori. Nonostante ciò, poichè gli output della ricerca sono spesso soluzioni complesse e di difficile implementazione, le soluzioni più usate in ambito industriale rimangono quelle ormai consolidate, come il controllo lineare ed il controllo ad isteresi. Un algoritmo di controllo per un azionamento elettrico dovrebbe essere semplice e di facile compresione. Inoltre dev'essere adatto ad applicazioni real-time. Robustezza ed affidabilità, oltre che alle performance, devono essere garantite, in particolare in applicazioni come gli elettrodomestici e l'automotive. Alla luce di ciò, il Controllo Predittivo rappresenta un valido candidato per introdurre vantaggi e miglioramenti in ambito industriale. Questa tecnica di controllo sfrutta un modello del sistema per predire il comportamento futuro delle variabili controllate. Questa informazione è utilizzata per scegliere l'azione di controllo migliore in base ad un criterio di ottimalità predefinito. Questo tipo di controllo è basato su idee che sono concettualmente semplici e intuitivi. Inoltre, l'implementazione della versione Finite Set risulta particolarmente facile. Oltre alla semplicitò gli altri vantaggi sono la possibilità di evitare la struttura in cascata (tipica del controllo lineare), le nonlinearità e le limitazioni possono essere direttamente incluse nel modello. Lo scopo di questa tesi è di studiare il controllo predittivo applicato al controllo di corrente di una macchina Sincrona a Riluttanza, analizzando ed affrontando alcune tematiche ancora aperte. In particolare, due aspetti sono considerati: la necessità di conoscere in modo preciso il modello della macchina e la possibilità di controllare la macchina lungo le traiettorie di MTPA, Flux-Weakening e MTPV.
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Muslim, Abrar. "Optimisation of chlorine dosing for water disribution system using model-based predictive control." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/459.
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An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation.In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1.To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.
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Muslim, Abrar. "Optimisation of chlorine dosing for water disribution system using model-based predictive control." Curtin University of Technology, Dept. of Chemical Engineering, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=21508.
Full textAbstract:
An ideal drinking water distribution system (DWDS) must supply safe drinking water with free chlorine residual (FCR) in the form of HOCI and OCIֿ at a required concentration level. Meanwhile the FCR is consumed in the bulk liquid phase and at the DWDS pipes wall as the result of chemical reactions. Because of these, an optimized chlorine dosing for the DWDS using model-based predictive control (MBPC) is developed through the steps of modelling the FCR transport along the main pipes of the DWDS, designing chlorine dosing and implementing a multiple-input multiple-output system control scheme in Matlab 7.0.1 software. Discrete time-space models (DTSM) that can be used to predict free chlorine residual (FCR) concentration along the pipes of the DWDS over time is developed using explicit finite difference method (EFDM). Simulations of the DTSM using step and rectangular pulse input show that the effect of water flow rate velocity is much stronger than the effect of chlorine effective diffusivity coefficient on the FCR distribution and decay process in the DWDS main pipes. Therefore, the FCR axial diffusion in single pipes of the DWDS can be neglected. Investigating the effect of injection time, initial chlorine distribution, and overall chlorine decay rate constant involved in the process have provided a thorough understanding of chlorination and the effectiveness of all the parameters. This study proposed a model-based chlorine dosing design (MBCDD) based on a conventional-optimum design process (CODP) (Aurora, 2004), which is created for uncertain water demand based on the DTSM simulation.In the MBCDD, the constraints must be met by designing distances between chlorine boosters and optimal value of the initial chlorine distribution in order to maintain the controlled variable (CV), i.e. FCR concentration with a certain degree of robustness to the variations of water flow rate. The MBCDD can cope with the simulated DWDS (SDWDS) with the conditions; the main pipe is 12 inch diameter size with the pipe length of 8.5 km, the first consumers taking the water from the point of 0.83 km, the assumed pipe wall chlorine decay rate constant of 0.45 m/day, and the value of chlorine overall decay rate constants follow Rosman's model (1994), by proposing a set of rules for selecting the locations for additional chlorine dosing boosters, and setting the optimal chlorine dosing concentrations for each booster in order to maintain a relatively even FCR distribution along the DWDS, which is robust against volumetric water supply velocity (VWS) variations. An example shows that by implementing this strategy, MBCDD can control the FCR along the 8.5 km main pipe of 12 inch diameter size with the VWS velocity from 0.2457 to 2.457 km/hr and with the assumed wall and bulk decay constants of 0.45 and 0.55 m/day, respectively. An adaptive chlorine dosing design (ACDD) as another CODP of chlorine dosing which has the same concept with the MBCDD without the rule of critical velocity is also proposed in this study. The ACDD objective is to obtain the optimum value of initial chlorine distribution for every single change in the VWS. Simulation of the ACDD on the SDWDS shows that the ACDD can maintain the FCR concentration within the required limit of 0.2-0.6 mg/1.
To enable water quality modelling for studying the effectiveness of chlorine dosing and injection in the form of mass flow rate of pure gaseous chlorine as manipulated variable (MV), a multiple-input multiple-output (MIMO) system is developed in Simulink for Matlab 7.0.1 software by considering the disturbances of temperature and circuiting flow. The MIMO system can be used to design booster locations and distribution along a main pipe of the DWDS, to monitor the FCR concentration at the point just before injection (mixing) and between two boosters, and to implement feedback and open-loop control. This study also proposed a decentralized model-based control (DMBC) based on the MBCDD-ACDD and centralized model predictive control (CMPC) in order to optimize MV to control the CV along the main pipe of the DWDS in the MIMO system from the FCR concentration at just after the chlorine injection (CVin) to the FCR concentration (CVo) before the next chlorine injection with the constraints of 0.2-0.6 ppm for both the CVin and CVo. A comparison of the performances of decentralized PI (DPI) control, DMBC and CMPC, shows that the performances of the DMBC and CMPC in controlling the MIMO system are almost the same, and they both are significantly better than the DPI control performance. In brief, model-based predictive control (MBPC), in this case a decentralized model-based control (DMBC) and a centralized predictive control (CMPC), enable optimization of chlorine dosing for the DWDS.
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Bonis, Ioannis. "Optimisation and control methodologies for large-scale and multi-scale systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/optimisation-and-control-methodologies-for-largescale-and-multiscale-systems(6c4a4f13-ebae-4d9d-95b7-cca754968d47).html.
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Distributed parameter systems (DPS) comprise an important class of engineering systems ranging from "traditional" such as tubular reactors, to cutting edge processes such as nano-scale coatings. DPS have been studied extensively and significant advances have been noted, enabling their accurate simulation. To this end a variety of tools have been developed. However, extending these advances for systems design is not a trivial task . Rigorous design and operation policies entail systematic procedures for optimisation and control. These tasks are "upper-level" and utilize existing models and simulators. The higher the accuracy of the underlying models, the more the design procedure benefits. However, employing such models in the context of conventional algorithms may lead to inefficient formulations. The optimisation and control of DPS is a challenging task. These systems are typically discretised over a computational mesh, leading to large-scale problems. Handling the resulting large-scale systems may prove to be an intimidating task and requires special methodologies. Furthermore, it is often the case that the underlying physical phenomena span various temporal and spatial scales, thus complicating the analysis. Stiffness may also potentially be exhibited in the (nonlinear) models of such phenomena. The objective of this work is to design reliable and practical procedures for the optimisation and control of DPS. It has been observed in many systems of engineering interest that although they are described by infinite-dimensional Partial Differential Equations (PDEs) resulting in large discretisation problems, their behaviour has a finite number of significant components , as a result of their dissipative nature. This property has been exploited in various systematic model reduction techniques. Of key importance in this work is the identification of a low-dimensional dominant subspace for the system. This subspace is heuristically found to correspond to part of the eigenspectrum of the system and can therefore be identified efficiently using iterative matrix-free techniques. In this light, only low-dimensional Jacobians and Hessian matrices are involved in the formulation of the proposed algorithms, which are projections of the original matrices onto appropriate low-dimensional subspaces, computed efficiently with directional perturbations.The optimisation algorithm presented employs a 2-step projection scheme, firstly onto the dominant subspace of the system (corresponding to the right-most eigenvalues of the linearised system) and secondly onto the subspace of decision variables. This algorithm is inspired by reduced Hessian Sequential Quadratic Programming methods and therefore locates a local optimum of the nonlinear programming problem given by solving a sequence of reduced quadratic programming (QP) subproblems . This optimisation algorithm is appropriate for systems with a relatively small number of decision variables. Inequality constraints can be accommodated following a penalty-based strategy which aggregates all constraints using an appropriate function , or by employing a partial reduction technique in which only equality constraints are considered for the reduction and the inequalities are linearised and passed on to the QP subproblem . The control algorithm presented is based on the online adaptive construction of low-order linear models used in the context of a linear Model Predictive Control (MPC) algorithm , in which the discrete-time state-space model is recomputed at every sampling time in a receding horizon fashion. Successive linearisation around the current state on the closed-loop trajectory is combined with model reduction, resulting in an efficient procedure for the computation of reduced linearised models, projected onto the dominant subspace of the system. In this case, this subspace corresponds to the eigenvalues of largest magnitude of the discretised dynamical system. Control actions are computed from low-order QP problems solved efficiently online.The optimisation and control algorithms presented may employ input/output simulators (such as commercial packages) extending their use to upper-level tasks. They are also suitable for systems governed by microscopic rules, the equations of which do not exist in closed form. Illustrative case studies are presented, based on tubular reactor models, which exhibit rich parametric behaviour.
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Polack, Philip. "Cohérence et stabilité des systèmes hiérarchiques de planification et de contrôle pour la conduite automatisée." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEM025/document.
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La voiture autonome pourrait réduire le nombre de morts et de blessés sur les routes tout en améliorant l'efficacité du trafic. Cependant, afin d'assurer leur déploiement en masse sur les routes ouvertes au public, leur sécurité doit être garantie en toutes circonstances. Cette thèse traite de l'architecture de planification et de contrôle pour la conduite automatisée et défend l'idée que l'intention du véhicule doit correspondre aux actions réalisées afin de garantir la sécurité à tout moment. Pour cela, la faisabilité cinématique et dynamique de la trajectoire de référence doit être assurée. Sinon, le contrôleur, aveugle aux obstacles, n'est pas capable de la suivre, entraînant un danger pour la voiture elle-même et les autres usagers de la route. L'architecture proposée repose sur la commande à modèle prédictif fondée sur un modèle bicyclette cinématique afin de planifier des trajectoires de référence sûres. La faisabilité de la trajectoire de référence est assurée en ajoutant une contrainte dynamique sur l'angle au volant, contrainte issue de ces travaux, afin d'assurer que le modèle bicyclette cinématique reste valide. Plusieurs contrôleurs à haute-fréquence sont ensuite comparés afin de souligner leurs avantages et inconvénients. Enfin, quelques résultats préliminaires sur les contrôleurs à base de commande sans modèle et leur application au contrôle automobile sont présentés. En particulier, une méthode efficace pour ajuster les paramètres est proposée et implémentée avec succès sur la voiture expérimentale de l'ENSIAME en partenariat avec le laboratoire LAMIH de ValenciennesAutonomous vehicles are believed to reduce the number of deaths and casualties on the roads while improving the traffic efficiency. However, before their mass deployment on open public roads, their safety must be guaranteed at all time.Therefore, this thesis deals with the motion planning and control architecture for autonomous vehicles and claims that the intention of the vehicle must match with its actual actions. For that purpose, the kinematic and dynamic feasibility of the reference trajectory should be ensured. Otherwise, the controller which is blind to obstacles is unable to track it, setting the ego-vehicle and other traffic participants in jeopardy. The proposed architecture uses Model Predictive Control based on a kinematic bicycle model for planning safe reference trajectories. Its feasibility is ensured by adding a dynamic constraint on the steering angle which has been derived in this work in order to ensure the validity of the kinematic bicycle model. Several high-frequency controllers are then compared and their assets and drawbacks are highlighted. Finally, some preliminary work on model-free controllers and their application to automotive control are presented. In particular, an efficient tuning method is proposed and implemented successfully on the experimental vehicle of ENSIAME in collaboration with the laboratory LAMIH of Valenciennes
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Afsi, Nawel. "Contrôle des procédés représentés par des équations aux dérivées partielles." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1033.
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L’objectif de ce travail est le contrôle des procédés représentés par des équations aux dérivées partielles. Deux procédés ont été considérés. Le premier procédé est un procédé de cristallisation en batch. L’objectif du contrôle est la génération d’une distribution des tailles de cristaux (DTC) ayant une taille moyenne adéquate. Tout d’abord, nous avons utilisé observateur à grand gain en cascade pour estimer cette taille moyenne en utilisant que la température du cristallisoir et la concentration du soluté. Ensuite, différents scénarios ont été testés afin de comparer les performances des différentes structures de la commande sans modèle. Le deuxième procédétraité est un procédé de polymérisation par ouverture de cycle du lactide. Cette réaction est très sensible aux impuretés. Alors, deux stratégies de contrôle ont été proposé afin de rétablir les conditions nominales en cas de dérive qui sont l’optimisation dynamique et la commande prédictiveThis work aims to control the processes represented by partial differential equations. Two processes were considered. The first process is a batch crystallization process. The aim of the control is to generate a crystal size distribution (CSD) with an appropriate mean size. First, we used a high gain cascade observer to estimate this average size using only the crystallizer temperature and solute concentration. Then, different scenarios were tested to compare the performance of the different structures of the control system without a model. The second process treated is a lactide polymerization process. This reaction is very sensitive to impurities. So, two control strategies were proposed to restore the nominal conditions in case of drift, which are the dynamic optimization and predictive control
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Pouilly-Cathelain, Maxime. "Synthèse de correcteurs s’adaptant à des critères multiples de haut niveau par la commande prédictive et les réseaux de neurones." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG019.
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Cette thèse porte sur la commande des systèmes non linéaires soumis à des contraintes non différentiables ou non convexes. L'objectif est de pouvoir réaliser une commande permettant de considérer tout type de contraintes évaluables en temps réel.Pour répondre à cet objectif, la commande prédictive a été utilisée en ajoutant des fonctions barrières à la fonction de coût. Un algorithme d'optimisation sans gradient a permis de résoudre ce problème d'optimisation. De plus, une formulation permettant de garantir la stabilité et la robustesse vis-à-vis de perturbations a été proposée dans le cadre des systèmes linéaires. La démonstration de la stabilité repose sur les ensembles invariants et la théorie de Lyapunov.Dans le cas des systèmes non linéaires, les réseaux de neurones dynamiques ont été utilisés comme modèle de prédiction pour la commande prédictive. L'apprentissage de ces réseaux ainsi que les observateurs non linéaires nécessaires à leur utilisation ont été étudiés. Enfin, notre étude s'est portée sur l'amélioration de la prédiction par réseaux de neurones en présence de perturbations.La méthode de synthèse de correcteurs présentée dans ces travaux a été appliquée à l’évitement d’obstacles par un véhicule autonomeThis PhD thesis deals with the control of nonlinear systems subject to nondifferentiable or nonconvex constraints. The objective is to design a control law considering any type of constraints that can be online evaluated.To achieve this goal, model predictive control has been used in addition to barrier functions included in the cost function. A gradient-free optimization algorithm has been used to solve this optimization problem. Besides, a cost function formulation has been proposed to ensure stability and robustness against disturbances for linear systems. The proof of stability is based on invariant sets and the Lyapunov theory.In the case of nonlinear systems, dynamic neural networks have been used as a predictor for model predictive control. Machine learning algorithms and the nonlinear observers required for the use of neural networks have been studied. Finally, our study has focused on improving neural network prediction in the presence of disturbances.The synthesis method presented in this work has been applied to obstacle avoidance by an autonomous vehicle
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Mohammadridha, Taghreed. "Automatic Glycemia Regulation of Type I Diabetes." Thesis, Ecole centrale de Nantes, 2017. http://www.theses.fr/2017ECDN0008.
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Cette thèse étudie le contrôle en boucle fermée pour la régulation de la glycémie du diabète de type 1 (DT1). Deux catégories principales de commande sont conçues: l'une est basée sur un modèle et l'autre non. Pour tester leur efficacité, les deux types sont testés in silico sur deux simulateurs de DT1. Le premier est un modèle à long terme qui est dérivé des données cliniques des sujets de DT1 et le second est le simulateur Uva/Padova. Tout d'abord, la commande sans modèle (CSM) est conçue. Après avoir montré qu'un régulateur proportionnel intelligent (iP) à référence constante peut être mis en défaut sur un simple second ordre, nous avons conçu un régulateur iP à référence variable. Une solution alternative est un régulateur proportionnel-intégral-dérivé intelligent (iPID) à référence constante. Une meilleure performance globale est obtenue avec iPID par rapport à iP et par rapport à un PID classique. Deuxièmement, une commande par modes glissants (CMG) garantie positive est conçue pour la première fois pour la régulation de la glycémie. La conception de cette commande est basée sur un modèle. La commande CMG est choisie pour la régulation de la glycémie en raison de ses propriétés de robustesse bien connues. Cependant, notre contribution majeure est l'assurance d'une commande rigoureusement positive. La commande CMG est conçue pour être positive partout dans un ensemble invariant du sous-système d'insulinémie du plasma. Enfin, un régulateur positif par retour d'état est calculé pour la première fois pour la régulation de la glycémie. Le plus grand ensemble positif invariant (EPI) est trouvé. Non seulement la positivité de la commande est révisée, mais plutôt un contrôle glycémique serré est atteint. Lorsque l'état initial du système appartient à l’EPI, l'hypoglycémie est évitée. Dans le cas contraire, l'hypoglycémie future est prédite pour tout état initial en dehors de l'EPIThis thesis investigates closed-loop control for glycemia regulation of Type1 Diabetes Mellitus (T1DM). Two main controller categories are designed: non-model-based and model-based. To test their efficiency, both types are tested in silico on two T1DM simulators. The first is a long-term model that is derived from clinical data of T1DM subjects and the second is the Uva/Padova simulator. Firstly, Model-free Control (MFC) is designed: a variable reference intelligent Proportional (iP) control and a constant reference intelligent Proportional-Integral-Derivative (iPID). Better overall performance is yielded with iPID over iP and over a classic PID. Secondly, a positive Sliding Mode Control SMC is designed for the first time for glycemia regulation. The model-based controller is chosen for glycemia regulation due to its well-known robustness properties. More importantly, our main contribution is that SMC is designed to be positive everywhere in the positively invariant set for the plasma insulin subsystem. Finally, a positive state feedback controller is designed for the first time to regulate glycemia. The largest Positively Invariant Set (PIS) is found. Not only control positivity is respected but rather a tight glycemic control is achieved. When the system initial condition belongs to the PIS, hypoglycemia is prevented, otherwise future hypoglycemia is predicted for any initial condition outside the PIS
Books on the topic "Offset-free model predictive control"
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Corcoran, Andrew W., and Jakob Hohwy. Allostasis, interoception, and the free energy principle: Feeling our way forward. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198811930.003.0015.
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Interoceptive processing is commonly understood in terms of the monitoring and representation of the body’s current physiological (i.e. homeostatic) status, with aversive sensory experiences encoding some impending threat to tissue viability. However, claims that homeostasis fails to fully account for the sophisticated regulatory dynamics observed in complex organisms have led some theorists to incorporate predictive (i.e. allostatic) regulatory mechanisms within broader accounts of interoceptive processing. Critically, these frameworks invoke diverse—and potentially mutually inconsistent—interpretations of the role allostasis plays in the scheme of biological regulation. This chapter argues in favor of a moderate, reconciliatory position in which homeostasis and allostasis are conceived as equally vital (but functionally distinct) modes of physiological control. It explores the implications of this interpretation for free energy-based accounts of interoceptive inference, advocating a similarly complementary (and hierarchical) view of homeostatic and allostatic processing.
Book chapters on the topic "Offset-free model predictive control"
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Tatjewski, Piotr. "Offset-Free Nonlinear Model Predictive Control." In Advances in Intelligent Systems and Computing, 33–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60699-6_5.
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Zhang, Guoqiang, Gaolin Wang, Nannan Zhao, and Dianguo Xu. "Starting Torque Control Strategy Based on Offset-Free Model Predictive Control Theory." In Permanent Magnet Synchronous Motor Drives for Gearless Traction Elevators, 123–40. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9318-2_7.
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Rybus, Tomasz, Karol Seweryn, and Jurek Z. Sąsiadek. "Nonlinear Model Predictive Control (NMPC) for Free-Floating Space Manipulator." In GeoPlanet: Earth and Planetary Sciences, 17–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94517-0_2.
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Liang, Ge, Wu Liao, Sheng Huang, Liu Long, Yu Liu, and Congqi Feng. "Model-Free Predictive Current Control of DTP-PMSM Based on Ultra-local Model." In Lecture Notes in Electrical Engineering, 1014–24. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1532-1_108.
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Huo, Da, Li Dai, Peizhan Wang, Ruochen Xue, and Yuanqing Xia. "Collision-Free Model Predictive Control for Periodic Trajectory Tracking of UAVs." In Lecture Notes in Electrical Engineering, 1291–300. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_128.
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Lin, Cheng-Kai, Jen-te Yu, Yen-Shin Lai, Hsing-Cheng Yu, Jyun-Wei Hu, and Dong-Yue Wu. "Two-Vectors-Based Model-Free Predictive Current Control of a Voltage Source Inverter." In Advanced Mechanical Science and Technology for the Industrial Revolution 4.0, 227–33. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4109-9_24.
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Jiang, Lin, Jun Deng, Yang Wang, Lu Han, and Pingyuan Li. "Sliding Mode Model-Free Predictive Current Control of PMSM with Direct Selection of Optimal Voltage Vector." In Lecture Notes in Electrical Engineering, 813–23. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1870-4_86.
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Huusom, Jakob Kjøbsted, Niels Kjølstad Poulsen, Sten Bay Jørgensen, and John Bagterp Jørgensen. "ARX-Model based Model Predictive Control with Offset-Free Tracking." In Computer Aided Chemical Engineering, 601–6. Elsevier, 2010. http://dx.doi.org/10.1016/s1570-7946(10)28101-4.
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"Improving robustness – the constraint free case." In Model-Based Predictive Control, 167–84. CRC Press, 2017. http://dx.doi.org/10.1201/9781315272610-9.
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"Model-Free Adaptive Predictive Control." In Model Free Adaptive Control, 179–214. CRC Press, 2013. http://dx.doi.org/10.1201/b15752-11.
Full textConference papers on the topic "Offset-free model predictive control"
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Borrelli, Francesco, and Manfred Morari. "Offset free model predictive control." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434770.
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Wallace, M., B. Das, P. Mhaskar, J. House, and T. Salsbury. "Offset-free model predictive controller for Vapor Compression Cycle." In 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6315409.
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Wallace, Matt, Prashant Mhaskar, John House, and Tim Salsbury. "Offset-free model predictive controller of a heat pump." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859114.
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Huusom, Jakob Kjobsted, Niels Kjolstad Poulsen, Sten Bay Jorgensen, and John Bagterp Jorgensen. "Adaptive disturbance estimation for offset-free SISO Model Predictive Control." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990909.
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Das, Buddhadeva, and Prashant Mhaskar. "Lyapunov-based offset-free model predictive control of nonlinear systems." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859472.
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Aghaee, Shahram, Yadollah Zakeri, and Farid Sheikholeslam. "Offset-free control of constrained linear systems using model predictive control." In 2008 IEEE International Symposium on Industrial Electronics (ISIE 2008). IEEE, 2008. http://dx.doi.org/10.1109/isie.2008.4677027.
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Nascu, Ioana, Richard Oberdieck, and Efstratios N. Pistikopoulos. "Offset-Free Explicit Hybrid Model Predictive Control of Intravenous Anaesthesia." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.433.
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Kuntz, Steven J., and James B. Rawlings. "Maximum Likelihood Estimation of Linear Disturbance Models for Offset-free Model Predictive Control." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867344.
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Ding, Baocang, Tao Zou, and Hongguang Pan. "A discussion on stability of offset-free linear model predictive control." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244013.
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Ingole, Deepak, Jan Drgona, and Michal Kvasnica. "Offset-free hybrid model predictive control of bispectral index in anesthesia." In 2017 21st International Conference on Process Control (PC). IEEE, 2017. http://dx.doi.org/10.1109/pc.2017.7976251.
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