Robustifying model predictive control of constrained linear systems

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Robustifying model predictive control of constrained linear systems

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Conventional model predictive control implements a version of receding horizon control where uncertainty (in the form of model error, estimation error, or disturbances) is absent. Robustness against uncertainty usually requires minimisation over control policies rather than control sequences; this may be prohibitively difficult in the presence of hard constraints on controls and states. It is shown how robustness may be achieved by a relatively simple modification to any conventional model predictive controller that is guaranteed to be stabilising in the absence of uncertainty.

Inspec keywords: optimal control; control system synthesis; discrete time systems; controllability; linear systems; robust control; predictive control

Other keywords: bounded disturbances; controllability set; hard constraints; uncertainty robustness; discrete-time system; robust model predictive control; constrained linear systems; receding horizon control

Subjects: Optimal control; Stability in control theory; Control system analysis and synthesis methods; Discrete control systems

References

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      • D.Q. Mayne . Control of constrained dynamic systems. Eur. J. Control , 87 - 99
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      • J.A. Rossiter , B. Kouvaritakis , M.J. Rice . A numerically robuststate-space approach to stable-predictive control strategies. Automatica , 1 , 65 - 73
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