Robust distributed model predictive consensus of discrete-time multi-agent systems:a self-triggered approach

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system(MAS)under bounded additive disturbances.We propose a self-triggered robust distributed model predictive control consensus algorithm.A new cost function is constructed and MAS is coupled through this function.Based on the proposed cost function,a self-triggered mechanism is adopted to reduce the communication load.Furthermore,to overcome additive disturbances,a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent.Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS.For each agent,we provide a concrete form of compatibility constraint and a consensus error terminal region.Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.