Decentralized control for a class of nonlinear interconnected systems using integral policy iteration

In this paper, we establish a decentralized control law to stabilize a class of nonlinear interconnected large-scale systems using a neural-network-based online model-free integral policy iteration algorithm. The model-free approach can solve the decentralized control problem for the interconnected system which has unknown dynamics. The stabilizing decentralized control strategy is derived based on the optimal control policies of the isolated subsystems. The online model-free integral policy iteration algorithm is developed to solve the optimal control problems for the isolated subsystems with unknown system dynamics. The actor-critic technique and the least squares implementation method are used to obtain the optimal control policies. A simulation example is given to verify the applicability of the decentralized control strategy.