Intelligent complementary sliding-mode control with dead-zone parameter modification
CF Hsu, TC Kuo - Applied Soft Computing, 2014 - Elsevier
This paper proposes an intelligent complementary sliding-mode control (ICSMC) system
which is composed of a computed controller and a robust controller. The computed
controller includes a neural dynamics estimator and the robust compensator is designed to
prove a finite L 2-gain property. The neural dynamics estimator uses a recurrent neural fuzzy
inference network (RNFIN) to approximate the unknown system term in the sense of the
Lyapunov function. In traditional neural network learning process, an over-trained neural …
which is composed of a computed controller and a robust controller. The computed
controller includes a neural dynamics estimator and the robust compensator is designed to
prove a finite L 2-gain property. The neural dynamics estimator uses a recurrent neural fuzzy
inference network (RNFIN) to approximate the unknown system term in the sense of the
Lyapunov function. In traditional neural network learning process, an over-trained neural …
[PDF][PDF] Intelligent Complementary Sliding-Mode Control with
CFHTC Kuo - IEEE Trans. Autom. Control, 2010 - academia.edu
This paper proposes an intelligent complementary sliding-mode control (ICSMC) system
which is composed of a computed controller and a robust controller. The computed
controller includes a neural dynamics estimator and the robust compensator is designed to
prove a finite 2 L-gain property. The neural dynamics estimator uses a recurrent neural fuzzy
inference network (RNFIN) to approximate the unknown system term in the sense of the
Lyapunov function. In traditional neural network learning process, an over-trained neural …
which is composed of a computed controller and a robust controller. The computed
controller includes a neural dynamics estimator and the robust compensator is designed to
prove a finite 2 L-gain property. The neural dynamics estimator uses a recurrent neural fuzzy
inference network (RNFIN) to approximate the unknown system term in the sense of the
Lyapunov function. In traditional neural network learning process, an over-trained neural …
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