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An Iterative Learning Control for Noisy Systems by Using Linear Neural Networks

  • Keigo Watanabe
  • Kozo Shiramizu
  • Toshio Fukuda
  • Spyros G. Tzafestas
Chapter
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)

Abstract

By applying a linear neural network, an iterative learning control is described for noisy CARMA (Controlled Autoregressive Moving Average) systems. The developed controller has a form of direct controller in a stochastic environment, while the usual iterative controller has a form of discrete observer in a determine-istic environment. Thus, the present iterative controller can treat with the case of noisy measurements. First, we describe the system considered here and state some assumptions to solve the problem. Next, the model of inverse dynamics is derived and the off-line iterative learning rule is explained by using the delta rule. Then, we show a neural controller, under the situation where the measurement noise is assumed to be known. Furthermore, we describe the method to estimate the noise process on-line by using the associated regular dynamics. Finally, we show some simulation examples to illustrate the features of the present controller. In particular, we shall discuss the results from the view points of the initial connec-tions weights and the generalizability of linear neural networks to some different or untrained reference signals.

Keywords

Inverse Dynamic Iterative Learning Iterative Learning Control Neural Network Controller Model Reference Adaptive Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • Keigo Watanabe
    • 1
  • Kozo Shiramizu
    • 1
  • Toshio Fukuda
    • 2
  • Spyros G. Tzafestas
    • 3
  1. 1.Department of Mechanical EngineeringFaculty of Science and Engineering, Saga UniversitySagaJapan
  2. 2.Department of Mechanical EngineeringFaculty of Engineering, Nagoya UniversityChikusa-ku, NagoyaJapan
  3. 3.Computer Science Division, National Technical University of AthensZografou, AthensGreece

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