Online Event-Triggered Adaptive Optimal Control of Nonlinear Large-Scale Systems

In this work, we proposed a new online decentralized event-triggered control method which is applicable to some of large-scale systems with nonlinear inter-connection affected by unknown inside system dynamics. This work first designs a recognizer based on neural network to rebuild the uncertain internal dynamics in interconnected system. In the presence of an event triggering mechanism, we next study an approximate optimal control method by adopting the adaptive critic learning method. In this paper, the decentralized event trigger conditions are influenced by only partial state messages of the relevant subsystems, so are controllers. Thus this approach eliminates some problems arising from the process of transmitting status information between subsystems via wireless communication networks. By using Lyapunov's theorem, we show that the state and critical weight estimation errors of the developed closed-loop control system are uniformly ultimately bounded. At last, two cases confirm the validity and suitability of the approach which designed in this paper.