Event-Triggered Decentralized Stabilization for State-Constrained Nonlinear Interconnected Systems Using Adaptive Dynamic Programming

In this paper, an event-triggered decentralized stabilization method based on adaptive dynamic programming (ADP) is proposed for nonlinear interconnected systems with constant-valued state constraints. By introducing a barrier function for coordinate transformation, the original system with state constraints is transformed into an unconstrained form. Then, with the developed cost functions for auxiliary subsystems, the decentralized stabilization problem of interconnected systems is transformed into a series of optimal control problems. Here-after, to obtain the event-triggered optimal control policies, the local policy iteration algorithm is investigated to solve the event-triggered Hamilton-Jacobi-Bellman equations (HJBEs) for auxiliary subsystems. The local critic neural networks (NNs) are employed to approximate the cost functions. Furthermore, the closed-loop nonlinear interconnected system and the weight estimation errors of local critic NNs are guaranteed to be uniformly ultimately bounded by a set of developed decentralized stabilizing policies. Finally, a numerical example is employed to validate the effectiveness of the proposed method.