Distributed min–max MPC for dynamically coupled nonlinear systems: A self-triggered approach

Abstract

This paper considers the formation stabilization problem of dynamically coupled nonlinear systems with parametric uncertainties and additive disturbances. We develop a distributed min–max model predictive control (MPC) framework, in which each subsystem adopts the local optimal control action by solving the constrained optimization problem. As a main contribution of this paper, a self-triggered strategy is presented within the proposed framework for resource-constrained coupled systems. By implementing the distributed self-triggered scheduler, the communication burden is significantly alleviated while ensuring comparable control performance. In addition, for each subsystem the latest information is transmitted to its neighbors asynchronously at the local triggering time instants. Moreover, the resulting distributed self-triggered min–max MPC framework ensures the constraints satisfaction and the closed-loop stability. Finally, the numerical experiments are performed to verify the theoretical results.