Distributed Lyapunov-Based Model Predictive Control with Neighbor-to-Neighbor Communication

摘要

This work considers distributed predictive control of large-scale nonlinear systems with neighbor-to-neighbor communication. It fulfills the gap between the existing centralized Lyapunov-based model predictive control (LMPC) and the cooperative distributed LMPC and provides a balanced solution in terms of implementation complexity and achievable performance. This work focuses on a class of nonlinear systems with subsystems interacting with each other via their states. For each subsystem, an LMPC is designed based on the subsystem model and the LMPC only communicates with its neighbors. At a sampling time, a subsystem LMPC optimizes its future control input trajectory assuming that the states of its upstream neighbors remain the same as (or close to) their predicted state trajectories obtained at the previous sampling time. Both noniterative and iterative implementation algorithms are considered. The performance of the proposed designs is illustrated via a chemical process example.