Multirate distributed model predictive control of nonlinear systems

In this work, we consider the design of a distributed model predictive control scheme using multirate sampling for large-scale nonlinear 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 and the network closes, to guarantee closed-loop stability. When the communication network is open, the distributed controllers operate in a decentralized fashion based only on local subsystem fast sampled state information to improve closed-loop performance. In the proposed design, the controllers are designed via Lyapunov-based model predictive control. 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.