Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics

- We present a synthesis approach of distributed MPC for nonlinear large-scale systems with dynamically decoupled dynamics.
- A new procedure for finding the neighbors has been proposed.
- The local quasi-infinite horizon predictive controller is designed, and the stability is analyzed.
- Simulation results show the effectiveness of the approach.

This paper considers the distributed model predictive control (MPC) of nonlinear large-scale systems with dynamically decoupled subsystems. According to the coupled state in the overall cost function of centralized MPC, the neighbors are confirmed and fixed for each subsystem, and the overall objective function is disassembled into each local optimization. In order to guarantee the closed-loop stability of distributed MPC algorithm, the overall compatibility constraint for centralized MPC algorithm is decomposed into each local controller. The communication between each subsystem and its neighbors is relatively low, only the current states before optimization and the optimized input variables after optimization are being transferred. For each local controller, the quasi-infinite horizon MPC algorithm is adopted, and the global closed-loop system is proven to be exponentially stable.