Distributed Optimization for Model Predictive Control of Linear Dynamic Networks

Eduardo Camponogara

This chapter presents existing models and distributed optimization algorithms for model predictive control (MPC) of linear dynamic networks (LDNs). The models consist of networks of subsystems with deterministic and uncertain dynamics subject to local and coupling constraints on the control and output signals. The distributed optimization algorithms are based on gradient-projection, subgradient, interior-point, and dual strategies that depend on the nature of the couplings and constraints of the underlying networks. The focus will be on a class of LDNs in which the dynamics of the subsystems are influenced by the control signals of the upstream subsystems with constraints on state and control variables. A distributed gradient-based algorithm is presented for implementing an interior-point method distributively with a network of agents, one for each subsystem.