Title :
Probabilistic Constrained MPC for Multiplicative and Additive Stochastic Uncertainty
Author :
Cannon, Mark ; Kouvaritakis, Basil ; Wu, Xingjian
Author_Institution :
Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK
fDate :
7/1/2009 12:00:00 AM
Abstract :
The technical note develops a receding horizon control strategy to guarantee closed-loop convergence and feasibility in respect of soft constraints. Earlier results addressed the case of multiplicative uncertainty only. The present technical note extends these to the more general case of additive and multiplicative uncertainty and proposes a method of handling probabilistic constraints. The results are illustrated by a simple design study considering the control of a wind turbine.
Keywords :
closed loop systems; machine control; predictive control; stochastic processes; stochastic systems; uncertain systems; wind turbines; additive stochastic uncertainty; closed-loop convergence; model predictive control; multiplicative stochastic uncertainty; probabilistic constrained MPC; probabilistic constraints; receding horizon control; soft constraints; wind turbine control; Automatic control; Constraint optimization; Convergence; Cost function; Equations; Fatigue; Motion control; Optimal control; Predictive control; Predictive models; Robust control; Robustness; Stochastic processes; Tracking; Trajectory; Uncertainty; Underwater vehicles; Wind turbines; Constrained control; fatigue life; optimization; stochastic control;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2009.2017970
