Tracking improvement for stable robot control using neural networks

This paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using an switch-type structure and an RBF neural network. It is shown that stab...

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Bibliographic Details
Main Authors: Feng, Gang, Palaniswami, M., Han, Z.X., Chak, C.K.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Artificial neural networks
Control systems
Equations
Manipulator dynamics
Neural networks
Performance gain
Robot control
Robot kinematics
Torque control
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Summary:This paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using an switch-type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.
DOI:10.1109/ICNN.1995.487736