Abstract:
This thesis presents the research undertaken to develop some novel learning algorithms
based on the sliding-mode control techniques for the neural network classifiers and
controllers. Although the feedforward neural network with the backpropagation learning
algorithm is the most widely used approach for classification and control applications,
the slow convergence rate. the local minima problem, the difficulties in system
identification and the lack of robustness are the issues existing for these neural networkbased
systems. The combination of the sliding-mode control techniques and the
backpropagation algorithm, as described in this thesis, leads to three novel learning
algorithms, which offer effective solutions for these problems.
The first learning algorithm, derived from the integration between the chattering-free
sliding-mode control technique and the backpropagation algorithm, can obtain fast and
global convergence with less computation. Experiment results relating to the headmovement
neural classifier for wheelchair control show that the proposed approach
considerably improved the convergence speed, global convergence capability and even
the generalisation performance of the neural network classifier, in comparison with
various popular learning algorithms.
The second learning algorithm, also derived from the integration between the
chattering-free sliding-mode control technique and the backpropagation algorithm, can
guarantee the stability and robustness of the neural control system with parameter
uncertainties. Based on this stable neural controller, a neural control design
methodology is developed for a class of uncertain nonlinear systems with transportation
lag, wherein a new training procedure is proposed to avoid the difficult choice of the
training inputs always associated with the conventional neural network identifier. The
implementation results with a real-time Static VAR Compensator system indicate the
effectiveness of the proposed method.
The third on-line learning algorithm, developed from the reaching law method
combined with the backpropagation algorithm, offers a robust adaptation approach for
the neural control systems with parameter uncertainties and disturbances. The neural
control approach is further developed to design a novel decentralised neural controller for a class of uncertain large-scale systems with bounds of interconnections and
disturbances. The stability and robustness of the neural control system are guaranteed
based on the Lyapunov synthesis. Real-time implementation results for a Coupled
Electric Drives CE8 system show the effectiveness and feasibility of the proposed
approach.
