Quasi-min-max optimization of dynamic output feedback robust MPC

For unknown system states in constrained linear parameter varying systems with bounded disturbances,a dynamic output feedback robust model predictive control approach via quasi-min-max robust optimization is designed.In the optimization problem,the dynamic output feedback controller takes a parameter-dependent form,and the optimization control problem can be formulated as convex optimization by the techniques of linear matrix inequalities.In the quasi-min-max robust optimization control problem,by constraining the current and predicted closed-loop system states to be within different robust positively invariant sets,and considering the exactly known model parameters at the current sampling time,the conservativeness of the designed dynamic output feedback controller parameters is reduced.Furthermore,the updates on real-time estimation error sets are performed by considering the invariance of the predicted closed-loop system states in the robust positively invariant set,which avoids the requirement of an auxiliary optimization to update estimation error sets in common output feedback robust model predictive control algorithms.The proposed algorithm not only improves the control performance and guarantees recursive feasibility of the optimization control problem,but also reduces the online computational burden on solving the optimization control problem.When the nominal closed-loop system is steered to the origin,the closed-loop system with bounded disturbances is stabilized within a region in the neighborhood of the origin.A simulation example is given to verify the effectiveness of the algorithm. © 2022 Science Press. All rights reserved.