Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming

@article{Sahoo2016NearOE,
  title={Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming},
  author={Avimanyu Sahoo and Hao Xu and Sarangapani Jagannathan},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2016},
  volume={27},
  pages={1801-1815}
}
This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically… CONTINUE READING
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