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Fuzzy modelling and robust control with applications to robotic manipulators

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Mei, F (1999) Fuzzy modelling and robust control with applications to robotic manipulators. PhD thesis, University of Tasmania.

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Abstract

In this thesis, fuzzy modelling of a class of nonlinear systems has been investigated
based on fuzzy logic and linear feedback control theory, and a few robust variable
structure control schemes for nonlinear systems have been developed. A number of
robustness and convergence results with dramatically reduced control chattering are
presented for variable structure control systems with applications to robotic
manipulators in the presence of parameter variations and external disturbances. The
major outcomes of the work described in this thesis are summarised as follows.
A robust tracking control scheme is proposed for a class of nonlinear systems with
fuzzy model. It is shown that a nominal system model for a nonlinear system is
established by fuzzy synthesis of a set of linearised local subsystems, where the
conventional linear feedback control technique is used to design a feedback controller
for the fuzzy nominal system. A variable structure compensator is then designed to
eliminate the effects of the approximation error and system uncertainties. Strong
robustness with respect to large system uncertainties and asymptotic convergence of
the output tracking error are obtained.
A sliding mode control scheme using fuzzy logic and Lyapunov stability theory has
been proposed. It is shown that a sliding mode is first designed to describe the desired
system dynamics for the controlled system. A set of fuzzy rules are then used to adjust
the controller's parameters based on the Lyapunov function and its time derivative.
The desired system dynamics are then obtained in the sliding mode. The sliding mode
controllers with fuzzy tuning algorithm show the advantage of reducing the chattering
of the control signals, compared with the conventional sliding mode controllers. A robust continuous sliding mode control scheme for linear systems with uncertainties
has been presented. The controller consists of three components: equivalent control,
continuous reaching mode control and robust control. It retains the positive properties
of sliding mode control but without the disadvantage of control chattering. The proposed control scheme has been applied to the tracking control of a one-link robotic
manipulator with fuzzy modelling of the nonlinear system.
A robust adaptive sliding mode control scheme with fuzzy tuning has been presented.
It is shown that an adaptive sliding mode control is first designed to learn the system
parameters with bounded system uncertainties and external disturbances. A set of
fuzzy rules are then used to adjust the controller's uncertainty bound based on the
Lyapunov function and its time derivative. The robust adaptive sliding mode
controller with fuzzy tuning algorithm show the advantage of reducing the chattering
and the amplitude of the control signals, compared with the adaptive sliding mode
controller without fuzzy tuning. Experimental example for a five-bar robot arm is
given in support of the proposed control scheme.
Finally, a new adaptive sliding mode controller has been developed for trajectory
tracking in robotic manipulators. This controller is able to estimate the constant part
of the system parameters as well as adaptively learn the uncertain part of the system
parameters by the Gaussian neural network. It is shown that under a mild assumption,
the proposed control law does not require measurement of acceleration signals. This
new control law exhibits the good aspects of Slotine and Li's (1987) and keeps the
chattering to a minimum level. An experiment of a five bar robotic system was done
and the results have confirmed the effectiveness of the approach.

Item Type: Thesis (PhD)
Keywords: Robust control, Fuzzy systems, Robotics
Copyright Holders: The Author
Copyright Information:

Copyright 1999 the author - The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s).

Additional Information:

Thesis (Ph.D.)--University of Tasmania, 2000. Includes bibliographical references

Date Deposited: 19 Dec 2014 02:40
Last Modified: 09 Jun 2016 00:43
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