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Tractable neurocontroller design and application to ship control with actuator limits

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3 Author(s)
Feng, W. ; Dept. of Electron. & Electr. Eng., Glasgow Univ., UK ; Yun Li ; Chong, G.

This paper extends the popular PID control structure to a nonlinear format by using a building block based neural network. A GA based-optimisation method is used to optimise the neurocontroller. Special training is employed in the design of a feedforward path neurocontroller, in which the network can be trained from a plant model directly. In order to arrive at the simplest structure of a network, the growth training method is developed. Through applications, it is found that if there is a rate limiter in a practical control loop, the automatically designed neurocontroller outperforms an optimised linear controller

Published in:

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th  (Volume:3 )

Date of Conference:

25-28 July 2001