Robust PID sliding mode control of robot manipulators with online learning neural network

Author

Pham Thuong Cat ; Nguyen Tran Hiep

Author_Institution

Inst. of Inf. Technol., Hanoi, Vietnam

fYear

2009

fDate

23-26 Aug. 2009

Firstpage

2187

Lastpage

2192

Abstract

The paper presents a new adaptive control algorithm for robot motion tracking problem to overcome noises and large uncertainties using integral sliding surface with a neural network. The control quality has been improved compared to conventional PD type sliding surface. It removes chattering and increases the accuracy. The weights of the neural network are updated continuously online granting the approximation of uncertain nonlinearities in the robot dynamics. The stability of the overall system has been proved by Lyapunov direct method. Computer simulations are given to illustrate the robustness and applicability of the proposed method.

Keywords

Lyapunov methods; adaptive control; control nonlinearities; learning (artificial intelligence); manipulator dynamics; motion control; neurocontrollers; radial basis function networks; robust control; variable structure systems; Lyapunov direct method; RFB neural network; adaptive control algorithm; computer simulation; control quality; integral sliding surface; online learning neural network; robot dynamics; robot manipulators; robot motion tracking problem; robust PID sliding mode control; robustness; system stability; uncertain nonlinearities; Decision support systems; Europe; Manipulators; Neural networks; Robustness; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2009 European

Conference_Location

Budapest

Print_ISBN

978-3-9524173-9-3

Type

conf

Filename

7074729