Robust Adaptive Fourier
Neural Network Control for Unknown Nonlinear Systems
Lilong Cai, Ph.D.
Department of Mechanical Engineering
University of Science and Technology, Hong Kong
The past two decades have witnessed major advances in neural
network based control, which has proven to be an efficient technique for
unknown nonlinear systems. However, due to heavy computation cost and slow
convergent speed, many existing NN may not be suitable for on-line control
applications. Moreover, both the determination of network topology and
stability analysis of closed-loop system are not easy to be carried out. These
drawbacks have prevented them from being widely used in control design.
In this talk, a robust adaptive Fourier neural network
(FNN) based control scheme is proposed for the control of unknown nonlinear
systems. A class of nonlinear systems to
be considered is described in a new reduced-order model in the state space. Via the new model, all the nonlinearities and
uncertainties of a system are included in a nonlinear function, and therefore
the control problem is converted into a function approximation problem. In
order to solve this problem and avoid the abovementioned drawbacks of
conventional NNs, a new type of NN, Fourier NN, is developed in the light of
complex Fourier analysis and neural network theory. Because the choice of basis
functions will strongly influence the performance of an NN, we employ orthogonal complex Fourier
exponentials as the basis functions of the FNN. Due to the orthogonality of the
basis functions, the FNN has a rich knowledge representation capability and is
suitable for real time control applications. In addition, since the FNN has a
clear physical meaning and is very close related to the frequency response
method, the determination of FNN topology and the selection of the parameters
of the basis functions become convenient for the designer.
Lilong Cai received his Bachelor degree in precision instrumentation
engineering, from Tianjin University, Tianjin,
China in 1982,
and his PhD degree in robotics from the University of Toronto, Canada, in 1990.
From 1990-1993, he worked as an assistant professor with the Department of
Mechanical Engineering at the Columbia University, New York City, NY.
. Since fall 1993, he has been with the
Department of Mechanical Engineering at the Hong Kong University of Science and
Technology and is now an associate professor.
Dr Cai 乪s
research interests lie in the control of nonlinear systems, robotics, optics and
mechatronics. He has published 42 referred international journal papers and 58 papers
at internationalconferences proceedings. He holds three US