Robust distributed model predictive control of constrained continuous-time nonlinear systems using two-layer invariant set

This paper investigates the distributed model predictive control (MPC) of a group of dynamically decoupled nonlinear systems coupled by cost function. The communication topology features neighbor-to-neighbor information exchange of the assumed system state trajectory. The cooperation among dynamically decoupled nonlinear systems is promoted by including a coupling term in the cost function. A control strategy is designed based on the two-layer invariant set in order to handle the effect of the external disturbances. It is shown that by appropriately choosing the sampling interval, the recursive feasibility is guaranteed provided that the initial state is feasible and the disturbance is bounded by a certain level. Sufficient conditions are established such that all system states converge to their corresponding robust positively invariant sets. The effectiveness of the proposed method is verified through a simulation example.