Abstract
An event-triggered robust distributed model predictive control (MPC) problem for multi-agent systems with bounded disturbances is studied in this paper. For each agent, a two-step triggering scheme is designed to decide when to solve its individual optimization problem, leading to reduced usage of computational resources. This scheme synthesizes a two-step event verification with a specified triggering condition and a waiting horizon based on a prediction model and a robust positively invariant set of the corresponding agent. The theoretical conditions for recursive feasibility and closed-loop robust stability are developed. The consensus among all agents is achieved. A simulation example is provided to show the effectiveness of the proposed approach.
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