Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Authors:
Fuchun Sun;Zengqi Sun
Affiliations:
National Laboratory of Intelligence Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R. China. E-mail: sfc@s1000e.cs.tsinghua.edu.cn;National Laboratory of Intelligence Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R. China. E-mail: sfc@s1000e.cs.tsinghua.edu.cn
Venue:
Journal of Intelligent and Robotic Systems
Year:
1997

Citing 4
Cited 1

Application of a general learning algorithm to the control of robotic manipulators

International Journal of Robotics Research
Stable adaptive control of robot manipulators using “neural” networks

Neural Computation
Multilayer discrete-time neural-net controller with guaranteed performance

IEEE Transactions on Neural Networks
Neural net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks

Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.