Coordinating multiple optimization-based controllers: New opportunities and challenges

Abstract

The status of using many, distributed optimization-based controllers for feedback control of large-scale, dynamic processes is presented and evaluated. We show that modeling the interactions between subsystems and exchanging trajectory information among subsystem model predictive controllers (MPCs) is insufficient to provide even closed-loop stability. The cause of this closed-loop instability is competition between the local agents. We next discuss the cooperative distributed MPC framework, in which the objective functions of the local MPCs are modified to achieve systemwide control objectives. This approach provides guaranteed nominal stability and performance properties, but at the cost of a high degree of communication between the local controllers. We next discuss the issue of taking advantage of the structure of the connections between the subsystems to reduce the required communication. The paper concludes by briefly presenting seven current and unsolved research challenges.