Robust model predictive control: reflections and opportunities

Goodwin, Graham C.; Kong, He; Mirzaeva, Galina; Serón, María M.

doi:10.1080/23307706.2014.913837

Cited by 50 publications

(13 citation statements)

References 101 publications

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“…In Algorithm 1, both the off-line and on-line optimization problems are formulated in terms of LMI constraints. Solving the optimization problems in Algorithm 1 is semidefinite programming, which (regarding the fastest interior-point algorithms) is proportional to 3 , where is the number of LMI scalar variables and is the number of LMI rows. 36 In the off-line optimization, problem (41)-(44) is solved r o times to search the parameters for ensuring initial feasibility of problem (45)-(46).…”

Section: Algorithm Complexity Analysismentioning

confidence: 99%

“…Robust model predictive control (MPC) is an effective technology for controlling complex plants characterized by uncertainties, nonlinearities, and constraints. [1][2][3] In the field of robust control, linear parameter varying (LPV) systems can approximate real nonlinear systems or represent uncertain systems by a polytopic family of linear systems, whose dynamics depend on time-varying scheduling parameters. 4,5 Over the last two decades, research activities in robust MPC for LPV systems have become an important branch, eg, state feedback robust MPC with measurable system states [6][7][8][9][10] and output feedback robust MPC with unknown system states.…”

Section: Introductionmentioning

confidence: 99%

“…Robust model predictive control (MPC) is an effective technology for controlling complex plants characterized by uncertainties, nonlinearities, and constraints . In the field of robust control, linear parameter varying (LPV) systems can approximate real nonlinear systems or represent uncertain systems by a polytopic family of linear systems, whose dynamics depend on time‐varying scheduling parameters .…”

Section: Introductionmentioning

confidence: 99%

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An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

Ping

Yang

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary The present paper addresses an observer‐based output feedback robust model predictive control for the linear parameter varying system with bounded disturbance and noise subject to input and state constraints. The main contribution is that the on‐line convex optimization problem not only simultaneously optimizes the observer and controller gains to stabilize the augmented closed‐loop system but also incorporates the refreshment of bounds of the estimation error set. The optimization problem steers the nominal augmented closed‐loop system to converge to the origin, and the real augmented closed‐loop system bounded within robust positive invariant set converges to a neighborhood of the origin such that recursive feasibility of the optimization and robust stability of the controlled system are ensured. Two numerical examples are given to illustrate the effectiveness of the method.

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Section: Algorithm Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

Ping

Yang

Wang

et al. 2019

Intl J Robust & Nonlinear

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“…Robust model predictive control (RMPC) has been an active research topic since more than three decades [1][2][3]. This is due to the fact that RMPC is capable of dealing with multi-variable systems, and allowing for system uncertainties and physical constraints to be considered in control sequence optimization in a straightforward manner.…”

Section: Introductionmentioning

confidence: 99%

A Convexity Approach to Dynamic Output Feedback Robust MPC for LPV Systems with Bounded Disturbances

Ping

Yang

Ding

et al. 2019

Int. J. Control Autom. Syst.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Computational resources can be an issue, as solving the optimization problem for larger (or nonlinear) systems can take significant time. Variants exist which deal with disturbances and model uncertainties in different ways [Bemporad and Morari 1999;Goodwin et al 2014].…”

Section: Time-based Control Techniquesmentioning

confidence: 99%

Control Strategies for Self-Adaptive Software Systems

Filieri

Maggio

Angelopoulos³

et al. 2017

ACM Trans. Auton. Adapt. Syst.

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The pervasiveness and growing complexity of software systems is challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms received a growing interest from the software engineering community in the last years for their mathematical grounding allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This paper discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and non-functional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the paper highlights a set of open challenges, both for the software engineering and the control theory research communities.

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Robust model predictive control: reflections and opportunities

Cited by 50 publications

References 101 publications

An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise

A Convexity Approach to Dynamic Output Feedback Robust MPC for LPV Systems with Bounded Disturbances

Control Strategies for Self-Adaptive Software Systems

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