Disturbance Rejection Self-Triggered Distributed MPC With Adaptive Prediction Horizon for Asynchronous Multiagent Systems

This article proposes a disturbance-observer-based self-triggered distributed model predictive control (DSDMPC) algorithm with an adaptive prediction horizon mechanism for discrete-time nonlinear multiagent systems (MASs) with disturbances and system constraints. First, decentralized discrete-time nonlinear disturbance observers are designed. They are combined with a space decomposition technique to concurrently estimate and eliminate the matched disturbances of MASs. Robust tightened state and control input constraints are generated based on the disturbance estimation information, Lipschitz continuity, and discrete Gronwall–Bellman inequality. Second, an self-triggered DMPC (SDMPC) algorithm with an adaptive prediction horizon mechanism is developed to restrain residual disturbances and robustly stabilize the disturbance-compensated MASs with aperiodic scheduling, asynchronous communication, and computational reduction. The recursive feasibility of the optimal control problem and closed-loop stability are discussed. Simulation results confirm the effectiveness of the proposed control algorithm.