An intelligent robust tracking control for electrically-driven robot systems

Authors:
Yeong-Chan Chang;Hui-Min Yen;Ming-Fang Wu
Affiliations:
Department of Electrical Engineering, Kun-Shan University, Taiwan, Republic of China;Department of Electrical Engineering, Kun-Shan University, Taiwan, Republic of China;Department of Electrical Engineering, Kun-Shan University, Taiwan, Republic of China
Venue:
International Journal of Systems Science
Year:
2008

Citing 11
Cited 2

Adaptive fuzzy systems and control: design and stability analysis

Adaptive fuzzy systems and control: design and stability analysis
Nonlinear Control of Electric Machinery

Nonlinear Control of Electric Machinery
Robust fuzzy logic control of mechanical systems

Fuzzy Sets and Systems - Theme: Fuzzy control
State observer-based robust control scheme for electrically driven robot manipulators

IEEE Transactions on Robotics
Fuzzy neural network quadratic stabilization output feedback control for biped robots via H∞ approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Global asymptotic stability of a tracking sectorial fuzzy controller for robot manipulators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive fuzzy sliding mode controller for robotic manipulators

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Stable adaptive control using fuzzy systems and neural networks

IEEE Transactions on Fuzzy Systems
Robust tracking enhancement of robot systems including motor dynamics: a fuzzy-based dynamic game approach

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

IEEE Transactions on Fuzzy Systems
Robust neural-network control of rigid-link electrically driven robots

IEEE Transactions on Neural Networks

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

Computers & Mathematics with Applications
Design of a robust neural network-based tracking controller for a class of electrically driven nonholonomic mechanical systems

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article addresses the problem of designing intelligent robust tracking controls of robot systems actuated by brushed direct current motors. The structures of both mechanical and electrical dynamics are allowed to be completely unknown and adaptive fuzzy (or neural network) systems are employed to approximate these two uncertainties. Consequently, an adaptive fuzzy-based (or neural network-based) state feedback tracking controller is developed such that the resulting closed-loop system guarantees that all the states and signals are bounded and the tracking error can be made as small as possible. Finally, simulation examples are made to demonstrate the effectiveness and tracking performance.