In this paper the potentials of neural network based control techniques are explored by applying a nonlinear generalized minimum variance control methodology to a simulated application example. In particular, reference is made to the control problem of regulating the output temperature of a liquid-satured steam heat exchanger by acting on the liquid flow rate. Due to the non minimum phase characteristic of the dynamics of the process, a simple inverting minimum variance controller is unsuitable. On the other hand, an effective solution is provided by a detuned model reference approach, which introduces a penalization factor in the control variable. A steady state off set error problem, caused by the neural network approximations, is tackled by means of an hybrid control structure, which combines a nonlinear integral action block with a neural controller. A comparison analysis is made to show the effectiveness of the proposed neural control schemes with respect to classical linear controllers.

Nonlinear identification and control of a heat exchanger: a neural network approach

BITTANTI, SERGIO;PIRODDI, LUIGI
1997-01-01

Abstract

In this paper the potentials of neural network based control techniques are explored by applying a nonlinear generalized minimum variance control methodology to a simulated application example. In particular, reference is made to the control problem of regulating the output temperature of a liquid-satured steam heat exchanger by acting on the liquid flow rate. Due to the non minimum phase characteristic of the dynamics of the process, a simple inverting minimum variance controller is unsuitable. On the other hand, an effective solution is provided by a detuned model reference approach, which introduces a penalization factor in the control variable. A steady state off set error problem, caused by the neural network approximations, is tackled by means of an hybrid control structure, which combines a nonlinear integral action block with a neural controller. A comparison analysis is made to show the effectiveness of the proposed neural control schemes with respect to classical linear controllers.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/559479
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