A nonlinear extension of minimum variance and generalised minimum variance control strategies is developed. The plant is modelled with a linear autoregressive part and a nonlinear dependency on the input. A neural network based implementation of the control law is discussed. This results in a nonlinear controller constituted by a few linear blocks complemented with not more than two neural networks. The weights of the networks are estimated off-line and the learning is carried out with input-output data provided by suitable open loop identification experiments. The performance of the time-invariant neuro-control system is compared with the one achievable by adaptive controllers based on linear models of the plant.

A GMV Technique for Nonlinear Control with Neural Networks

BITTANTI, SERGIO;PIRODDI, LUIGI
1994

Abstract

A nonlinear extension of minimum variance and generalised minimum variance control strategies is developed. The plant is modelled with a linear autoregressive part and a nonlinear dependency on the input. A neural network based implementation of the control law is discussed. This results in a nonlinear controller constituted by a few linear blocks complemented with not more than two neural networks. The weights of the networks are estimated off-line and the learning is carried out with input-output data provided by suitable open loop identification experiments. The performance of the time-invariant neuro-control system is compared with the one achievable by adaptive controllers based on linear models of the plant.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/527465
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