Almost decentralized lyapunov-based nonlinear model predictive control

This paper proposes an almost decentralized solution to the problem of stabilizing a network of discrete-time nonlinear systems with coupled dynamics that are subject to local state/input constraints. By “almost decentralized” we mean that each local controller is allowed to use the states of neighboring systems for feedback, whereas it is not permitted to employ iterations between the systems in the network to compute the control action. The controller synthesis method used in this work is Lyapunov-based model predictive control (MPC). The stabilization conditions are decentralized via a set of structured control Lyapunov functions (CLFs) for which the maximum over all the functions in the set is a CLF for the global network of systems. However, this does not necessarily imply that each function is a CLF for its corresponding subsystem. Additionally, we provide a solution for relaxing the temporal monotonicity of the CLF for the overall network. For structured CLFs defined using the infinity norm, we show that the decentralized MPC algorithm can be implemented by solving a single linear program in each network node. A nontrivial example illustrates the effectiveness of the developed theory and shows that the proposed method can perform as well as more complex distributed, iteration-based MPC algorithms.