Robust Model Predictive Control
Peter J. Campo,Manfred Morari +1 more
- Iss: 24, pp 1021-1026
TLDR
It is shown that the required minimax optimization can be recast as a linear program for uncertainty descriptions which provide impulse response models as affine functions of uncertain parameters.Abstract:
Concepts of model predictive control are extended to uncertain linear systems. An on-line optimizing control scheme is developed which has as its objective the minimization of the worst-case tracking error for a family of linear plants. For uncertainty descriptions which provide impulse response models as affine functions of uncertain parameters, it is shown that the required minimax optimization can be recast as a linear program. Situations which lead to such an uncertainty description are discussed. An example is presented to demonstrate the properties of the proposed control scheme.read more
Citations
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Journal ArticleDOI
Robust constrained model predictive control using linear matrix inequalities
TL;DR: This paper presents a new approach for robust MPC synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation, and shows that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants.
Journal ArticleDOI
Robust model predictive control of constrained linear systems with bounded disturbances
TL;DR: This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances by solving the optimal control problem that is solved online.
Journal ArticleDOI
Systems with persistent disturbances: predictive control with restricted constraints
TL;DR: Predictive regulation of linear discrete-time systems subject to unknown but bounded disturbances and to state/control constraints and an algorithm based on constraint restrictions is presented and its stability properties are analysed.
Proceedings ArticleDOI
Robust constrained model predictive control using linear matrix inequalities
TL;DR: In this article, the authors address the robustness issue in MPC by directly incorporating the description of plant uncertainty in the MPC problem formulation, where the plant uncertainty is expressed in the time domain by allowing the state-space matrices of the discrete-time plant to be arbitrarily time-varying and belonging to a polytope.
References
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Journal ArticleDOI
Robust control of ill-conditioned plants: high-purity distillation
TL;DR: In this article, a high-purity distillation column is used to explain the physical reason for poor conditioning and its implications on control system design and performance, and it is shown that an acceptable performance/robustness tradeoff cannot be obtained by simple loop-shaping techniques (using singular values) and that a good understanding of the model uncertainty is essential for robust control systems design.
Unifying framework for control system design under uncertainty and its implications for chemical process control
M. Morari,J.C. Doyle +1 more
TL;DR: In this paper a general approach is outlined for the assessment of the effects of model uncertainty on control system performance and methods for the design of controllers which meet given performance specifications despite model uncertainty are discussed.
Proceedings ArticleDOI
norm formulation of model predictive control problems
Peter J. Campo,Manfred Morari +1 more
TL;DR: A general mathematical programming framework for multivariable model predictive control problems is presented and a new formulation, based on the ∞-norm, is introduced and developed.