Plenary Lecture
Simultaneous Perturbation Optimization Method for Adaptive Control
Professor Yutaka Maeda
Faculty of Engineering Science
3-3-35, Yamate-cho, Suita, Kansai University
Japan
E-mail: maedayut@kansai-u.ac.jp
Abstract: The simultaneous perturbation optimization method is a stochastic gradient method which uses only values of an objective function to find a optimal point of the function. The optimization method was introduced by J. C. Spall. Y. Maeda also independently proposed a learning rule using the simultaneous perturbation and reported a feasibility of the learning rule. At the same time, the merit of the learning rule was demonstrated in the hardware implementation of neural networks. Convergence conditions of the method in framework of the stochastic approximation are also shown by Spall.
The most important advantage of the simultaneous perturbation method is its simplicity. The simultaneous perturbation can estimate the gradient of a function using only the two values of the function. Therefore, it is relatively easy to implement as an optimization method, compared with the other gradient types of optimization methods. Moreover even if the function is not differentiable partly, we can apply the method.
This paper presents a parameter adjustment rule using simultaneous perturbation for Model reference adaptive control system (MRACS). Using the simultaneous perturbation method, we can construct a rule without the sensitivity derivatives of an objective plant. This feature is beneficial when properties of the plant are unknown or changing. We apply the proposed method to control a two-link flexible arm. The motion control of the arms is considered.
Brief Biography of the Speaker:
Yutaka Maeda received the B.E., M.E. and D.E. (Doctor of Engineer) degrees in Electronic Engineering from Osaka Prefecture University in 1979, 1981 and 1990, respectively. He joined KANSAI University, Faculty of Engineering in 1987, where he is a Dean and Professor of the Faculty of Engineering Science, Kansai University. Moreover he is also a Trustee of Kansai University.
He was a Visiting Researcher in Electrical and Computer Engineering Department, University of California at Irvine, USA in 1995. He has established the Electronic Control Laboratory in Kansai University. The laboratory is producing promising graduates for many industrial fields. Recent research interests in this laboratory are in the areas of soft computing; artificial neural networks, fuzzy theory for robot control, moreover, he is also interesting in the control theory and signal processing related to the simultaneous perturbation optimization. He is also author of about 80 papers in international journals and conference proceedings, and book chapters.