Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems

In this work, we consider the design of a network-based distributed model predictive control system using multirate sampling for large-scale nonlinear uncertain systems composed of several coupled subsystems. Specifically, we assume that the states of each local subsystem can be divided into fast sampled states (which are available every sampling time) and slowly sampled states (which are available every several sampling times). The distributed model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion at time instants in which full system state measurements (both fast and slow) are available, to guarantee closed-loop stability. When local subsystem fast sampled state information is only available, the distributed controllers operate in a decentralized fashion to improve closed-loop performance. In the proposed control architecture, the controllers are designed via Lyapunov-based model predictive control techniques taking into account bounded measurement noise, process disturbances and communication noise. Sufficient conditions under which the state of the closed-loop system is ultimately bounded in an invariant region containing the origin are derived. The theoretical results are demonstrated through a nonlinear chemical process example.

► Design of distributed model predictive control systems using multirate sampling for nonlinear systems.
► Guaranteed closed-loop stability properties and explicit consideration of model uncertainty and noise.
► Application to a chemical process network example.