Event-Triggered Distributed MPC for Cyber-Physical Systems: An Adaptive Dual-Horizon Mechanism

This paper aims to propose a new event-triggered distributed model predictive control framework based on an adaptive dual-horizon (prediction and control horizon) mechanism for multi-agent cyber-physical systems (CPS) with additive disturbances. Firstly, the control horizon is introduced into the constrained optimal control problem (OCP) of multi-agent CPS to reduce the number of independent variables and improve the rapidity of the algorithm. Secondly, a periodic event-triggered mechanism with Zeno-free behavior is designed based on state error information, which can more efficiently reduce the frequency of solving OCP. Then, adaptive prediction horizon and control horizon contraction mechanisms are designed, effectively reducing the computational complexity of solving OCP for the controller at the triggering moment. In addition, sufficient conditions are provided through theoretical analysis to guarantee the recursive feasibility of algorithm and the stability of the closed-loop system. Finally, the effectiveness of the proposed algorithm is verified through simulation and experimentation example of networked multi-robot system, and the results showed that the algorithm can effectively reduce communication resource consumption and computational complexity in solving OCP while ensuring the expected cooperative control effect.