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This paper presents an inverse optimal neural controller, which is constituted by the combination of two well known techniques: (a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and (b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to...
This paper deals with the problem of trajectory tracking for delayed recurrent neural networks. The tracking error is global asymptotic stabilized by a control law derived on the basis of a Lyapunov-Krasovsky functional. Then, it is established that this control law minimizes a meaningful cost functional. Applicability of the approach is illustrated by means of an example
This paper reports a hardware implementation for real-time global asymptotic stabilization of the chaotic Chen's system from the inverse optimal control approach
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