Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems

Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems

Abstract

We consider the problem of stabilization of a linear system, under state and control constraints, and subject to bounded disturbances and unknown parameters in the state matrix. First, using a simple least square solution and available noisy measurements, the set of admissible values for parameters is evaluated. Second, for the estimated set of parameter values and the corresponding linear interval model of the system, two interval predictors are recalled and an unconstrained stabilizing control is designed that uses the predicted intervals. Third, to guarantee the robust constraint satisfaction, a model predictive control algorithm is developed, which is based on solution of an optimization problem posed for the interval predictor. The conditions for recursive feasibility and asymptotic performance are established. Efficiency of the proposed control framework is illustrated by numeric simulations.

Publication
Proceedings of the 59th Conference on Decision and Control (CDC 2020)
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Edouard Leurent
PhD Student in Reinforcement Learning

My research interests include control, statistical learning and robotics.