Neuroadaptive control of elastic-joint robots using robust performance enhancement

Authors:
C. J. B. Macnab;G. M. T. D'eleuterio
Affiliations:
University of Toronto Institute for Aerospace Studies, 4925 Dufferin St., Downsview, ON, M3H 5T6 (Canada);University of Toronto Institute for Aerospace Studies, 4925 Dufferin St., Downsview, ON, M3H 5T6 (Canada)
Venue:
Robotica
Year:
2001

Citing 8
Cited 2

On the adaptive control of robot manipulators

International Journal of Robotics Research
Stable adaptive systems

Stable adaptive systems
Global tracking controllers for flexible-joint manipulators: a comparative study

Automatica (Journal of IFAC)
Robust adaptive control

Robust adaptive control
Robust nonlinear control design: state-space and Lyapunov techniques

Robust nonlinear control design: state-space and Lyapunov techniques
Robot Dynamics and Control

Robot Dynamics and Control
Nonlinear and Adaptive Control Design

Nonlinear and Adaptive Control Design
Robust Tracking Control of Robot Manipulators

Robust Tracking Control of Robot Manipulators

A manoeuvre control strategy for flexible-joint manipulators with joint dry friction

Robotica
Improved Output Tracking of a Flexible-Joint Arm using Neural Networks

Neural Processing Letters

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Abstract

A neuroadaptive control scheme for elastic-joint robots is proposed that uses a relatively small neural network. Stability is achieved through standard Lyapunov techniques. For added performance, robust modifications are made to both the control law and the weight update law to compensate for only approximate learning of the dynamics. The estimate of the modeling error used in the robust terms is taken directly from the error of the network in modeling the dynamics at the currant state. The neural network used is the CMAC-RBF Associative Memory (CRAM), which is a modification of Albus's CMAC network and can be used for robots with elastic degrees of freedom. This results in a scheme that is computationally practical and results in good performance.