Robust Adaptive Dynamic Programming for Large‐Scale Systems
Yu Jiang, Zhong‐Ping Jiang · 2017
This chapter explains the robust adaptive dynamic programming (RADP) theory for the decentralized optimal control of a generalized class of large-scale systems. The controller design of each subsystem only utilizes local state variables, without knowing the system dynamics. By integrating a simple version of the cyclic-small-gain theorem, asymptotic stability can be achieved by assigning appropriate weighting matrices for each subsystem. As a by-product, certain suboptimality properties can be obtained. The chapter describes the class of large-scale uncertain systems to be studied. Then, an RADP-based decentralized optimal controller design scheme is presented. It is also shown that the closed-loop interconnected system enjoys some suboptimality properties. In addition, the effectiveness of the proposed methodology is demonstrated via its application to the online learning control of a ten-machine power system with governor controllers.