Implementation of Lyapunov learning algorithm for fuzzy switching adaptive controller modeled under Quasi-ARX Neural Network

This paper presents a fuzzy adaptive controller applied to a non linear system modeled under a Quasi-linear ARX Neural Network, with stability proof by using the Lyapunov approach. This work exploits the new idea to use Lyapunov function to train multi-input multi-output neural network on the core-p...

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Bibliographic Details
Main Authors: Sutrisno, Imam, Jami'in, Mohammad Abu, Jinglu Hu
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Fuzzy Switching Adaptive Controller
Lyapunov Learning Algorithm
Lyapunov methods
Neural networks
Predictive models
Quasi-ARX Neural Network
Stability analysis
Switches
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Summary:This paper presents a fuzzy adaptive controller applied to a non linear system modeled under a Quasi-linear ARX Neural Network, with stability proof by using the Lyapunov approach. This work exploits the new idea to use Lyapunov function to train multi-input multi-output neural network on the core-part sub-model. The proposed controller is designed between a non linear controller and linear controller based on fuzzy switching algorithm. Finally improving performances of the Lyapunov learning algorithm are stable in the learning process, fast convergence of error, and able to increase the accuracy of the controller.
DOI:10.1109/MIC.2013.6758071