Distributed Model Predictive Control for Linear Constrained Systems Based on Time-Varying Terminal Sets

A novel distributed model predictive control (DMPC) strategy with time-varying terminal set for linear constrained systems is presented in this paper. To decrease the load of computation of DMPC while ensuring the global optimization, the nominal system is introduced by treating the influence of neighboring subsystems as a bounded disturbance. Then, under the distributed control structure, a distributed predictive control optimization problem containing the nominal state and input can be designed for each subsystem. Furthermore, different from most DMPC approaches, a novel approach to design a terminal constraint set that can be updated in every update time based on the predicted state of the system is proposed. Additionally, the analysis of feasibility and the stability of the proposed DMPC algorithm are described under kinds of the system constraints. Finally, experimental simulation is shown to prove validity by the control scheme in this paper.

Abstract licence: CC BY-NC-ND

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