Neural networks for control

  • Johan A. K. Suykens
  • Joos P. L. Vandewalle
  • Bart L. R. De Moor
Chapter

Abstract

In this Chapter we provide the reader with some background material on neural control strategies and we discuss neural optimal control in more detail. The Chapter is organized as follows. In Section 4.1 the basic principles of existing methods in neural control are presented, including direct and indirect adaptive control, reinforcement learning, neural optimal control, internal model control and model predictive control. In Section 4.2 neural optimal control is discussed with respect to classical theory of nonlinear optimal control. The emphasis in this Section is on the formulation of control problems as parametric optimization problems, where static and dynamic nonlinear controllers are parametrized by multilayer perceptrons. Furthermore an efficient way for including a priori results from linear control theory into the neural controller is highlighted. The latter is illustrated on the examples of swinging up an inverted and double inverted pendulum system. New contributions are stated in Section 4.2.6.

Keywords

Model Predictive Control Internal Model Control Neural Controller Static Output Feedback Neural 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 1996

Authors and Affiliations

  • Johan A. K. Suykens
    • 1
  • Joos P. L. Vandewalle
    • 1
  • Bart L. R. De Moor
    • 1
  1. 1.K.U. LeuvenBelgium

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