Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements

Authors:
Hui-Min Yen;Tzuu-Hseng S. Li;Yeong-Chan Chang
Affiliations:
Department of Electrical Engineering, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan, ROC;Department of Electrical Engineering, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan, ROC;Department of Electrical Engineering, Kun-Shan University, 949, Da-Wan Road, Yung-Kang District, Tainan 71003, Taiwan, ROC
Venue:
Computers & Mathematics with Applications
Year:
2012

Citing 11
Cited 1

Robust adaptive control

Robust adaptive control
Control of Robot Manipulators

Control of Robot Manipulators
An intelligent robust tracking control for electrically-driven robot systems

International Journal of Systems Science
Sliding mode synchronization of an uncertain fractional order chaotic system

Computers & Mathematics with Applications
Fuzzy logic combining controller design for chaos control of a rod-type plasma torch system

Expert Systems with Applications: An International Journal
Design of a fuzzy sliding-mode synchronization controller for two different chaos systems

Computers & Mathematics with Applications
On the optimal shape parameter for Gaussian radial basis function finite difference approximation of the Poisson equation

Computers & Mathematics with Applications
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem

Computers & Mathematics with Applications
State observer-based robust control scheme for electrically driven robot manipulators

IEEE Transactions on Robotics
Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach

IEEE Transactions on Fuzzy Systems
Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

IEEE Transactions on Neural Networks

Genetically evolved action selection mechanism in a behavior-based system for target tracking

Neurocomputing

Quantified Score

Hi-index	0.09

Visualization

Abstract

This paper addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by plant uncertainties and external disturbances. Adaptive neural network systems are employed to approximate the behaviors of uncertain mechanical and electrical dynamics. A reduced-order observer is constructed to estimate the velocity signals. Only the measurements of link position and armature current are required for feedback. Consequently, an adaptive neural network-based dynamic feedback tracking controller without velocity measurements is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking errors can be made as small as possible. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.