Title :
Neural feedback linearization and its application in radar servo systems
Author :
Shuntian, Lou ; Chen Xinbai ; Xianda, Zhang
Author_Institution :
Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China
Abstract :
This paper proposes a feedback linearized design method for a kind of affine nonlinear system with unknown nonlinearity and/or uncertainty. A neural network is used to approximate the unknown function and/or uncertainty in the system. Then the neural feedback linearized controller without off-line training is obtained using conventional feedback linearization. According to the Lyapunov stability theory, the weight update law of neural network, the adaptive law of error bound and the feedback control are obtained, thus the stability of closed-loop control system is ensured. This design method can be applied to radar servo system. The novel radar servo system using neural feedback linearization is constructed and its effectiveness is verified through simulations
Keywords :
Lyapunov methods; closed loop systems; feedback; function approximation; linearisation techniques; neurocontrollers; nonlinear systems; radar; servomechanisms; stability; uncertain systems; Lyapunov stability; affine nonlinear system; closed-loop system; feedback; function approximation; linearization; neural network; neurocontrol; radar servo systems; uncertain system; Adaptive systems; Design methodology; Linear feedback control systems; Lyapunov method; Neural networks; Neurofeedback; Nonlinear systems; Radar applications; Servomechanisms; Uncertainty;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863390
