This paper presents an autotuning method for industrial PID controllers in the 1-d.o.f. ISA form. The major feature of the method is that the model structure employed for the process is selected on-line based on a step response record, by means of a multilayer perceptron neural network. Thanks to the exclusive use of normalized I/O data, the network can be trained off-line with simulated data, therefore simplifying the method’s implementation. Once the model structure is selected and its parameters are identified, the IMC approach is used for synthesizing a regulator that is then approximated with a PID. Simulation and experimental results are reported to show the effectiveness of the proposed tuning method and its advantages with respect to IMC-based PID tuning with the model structure fixed a priori.

Model-based PID autotuning enhanced by neural structural identification

LEVA, ALBERTO;PIRODDI, LUIGI
2004-01-01

Abstract

This paper presents an autotuning method for industrial PID controllers in the 1-d.o.f. ISA form. The major feature of the method is that the model structure employed for the process is selected on-line based on a step response record, by means of a multilayer perceptron neural network. Thanks to the exclusive use of normalized I/O data, the network can be trained off-line with simulated data, therefore simplifying the method’s implementation. Once the model structure is selected and its parameters are identified, the IMC approach is used for synthesizing a regulator that is then approximated with a PID. Simulation and experimental results are reported to show the effectiveness of the proposed tuning method and its advantages with respect to IMC-based PID tuning with the model structure fixed a priori.
Proceedings of the American Control Conference
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/257735
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