<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper has designed a variable structure controller based on the nominal compensation of neural networks. The neural network input is the desired trajectory, which eliminates the strict assumptions of the control inputs in conventional neural networks. It also ensures the asymptotic stability of the system closed-loop global exponentials to introduce model compensation and continuous variable structure control rate. By means of Lyapunov stability theory, it is analyzed and researched how to guarantee good transient performance of the control system comprehensively and thoroughly. The theoretic analysis and simulation results demonstrate the efficiency of the method proposed.</p></abstract>

This paper has designed a variable structure controller based on the nominal compensation of neural networks. The neural network input is the desired trajectory, which eliminates the strict assumptions of the control inputs in conventional neural networks. It also ensures the asymptotic stability of the system closed-loop global exponentials to introduce model compensation and continuous variable structure control rate. By means of Lyapunov stability theory, it is analyzed and researched how to guarantee good transient performance of the control system comprehensively and thoroughly. The theoretic analysis and simulation results demonstrate the efficiency of the method proposed.

Research Design Of An Adaptive Controller Based On Desired Trajectory Compensation Of Neural Networks

School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169 China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper has designed a variable structure controller based on the nominal compensation of neural networks. The neural network input is the desired...

Research Design Of An Adaptive Controller Based On Desired Trajectory Compensation Of Neural Networks

Published Online: Dec 30, 2014

Page range: 5 - 16

DOI: https://doi.org/10.2478/cait-2014-0039

© by Liu Yu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.