Adaptive control of robot manipulators with neural network based compensation of frictional uncertainties

Authors:
M. Kemal Ciliz
Affiliations:
Electrical and Electronics Engineering Department, Bogazici University, Bebek, Istanbul (Turkey)
Venue:
Robotica
Year:
2005

Citing 11
Cited 7

Adaptive control of mechanical manipulators

International Journal of Robotics Research
On the adaptive control of robot manipulators

International Journal of Robotics Research
Stable adaptive systems

Stable adaptive systems
Composite adaptive control of robot manipulators

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Neural networks for control

Neural networks for control
Adaptive friction compensation in robot manipulators: low velocities

International Journal of Robotics Research
A survey of models, analysis tools and compensation methods for the control of machines with friction

Automatica (Journal of IFAC)
Adaptive control of robotic manipulators using multiple models and switching

International Journal of Robotics Research
Neural Network Control of Robot Manipulators and Nonlinear Systems

Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook

Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
On-line learning control of manipulators based on artificial neural network models

Robotica

Comparative experiments with a multiple model based adaptive controller for a SCARA type direct drive manipulator

Robotica
Friction modelling and compensation for motion control using hybrid neural network models

Engineering Applications of Artificial Intelligence
Brief paper: Adaptive control of robot contact tasks with on-line learning of planar surfaces

Automatica (Journal of IFAC)
Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots

Robotics and Computer-Integrated Manufacturing
A manoeuvre control strategy for flexible-joint manipulators with joint dry friction

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Neural network-based sliding mode adaptive control for robot manipulators

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Neural network-based adaptive position tracking control for bilateral teleoperation under constant time delay

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Abstract

In this paper a framework is proposed for the adaptive control of robotic manipulators which combines parametric adaptive control with Artificial Neural Network (ANN)-based compensation of dynamic uncertainties like friction. The proposed method utilizes a passivity-based parametric adaptive control approach and makes use of the ANN models as generic identifiers to compensate for unmodelled friction effects. Unlike many approaches for ANN based control in the literature, parameter update equations for the ANN model and for the parametric adaptive model are driven by both the tracking error and the system identification error. A stability analysis is given based on the passivity properties of the manipulator dynamics. The methodology is successfully tested for the control of a Direct Drive SCARA arm and performance is compared with standard adaptive control schemes.