Minimax design of neural net controllers for highly uncertain plants

  • Authors:

  • A. V. Sebald;J. Schlenzig

  • Affiliations:

  • Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA;-

  • Venue:
  • IEEE Transactions on Neural Networks
  • Year:
  • 1994

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Abstract

This paper discusses the use of evolutionary programming (EP) for computer-aided design and testing of neural controllers applied to problems in which the system to be controlled is highly uncertain. Examples include closed-loop control of drug infusion and integrated control of HVAC/lighting/utility systems in large multi-use buildings. The method is described in detail and applied to a modified Cerebellar Model Arithmetic Computer (CMAC) neural network regulator for systems with unknown time delays. The design and testing problem is viewed as a game, in that the controller is chosen with a minimax criterion i.e., minimize the loss associated with its use on the worst possible plant. The technique permits analysis of neural strategies against a set of feasible plants. This yields both the best choice of control parameters and identification of that plant which is most difficult for the best controller to handle