This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

A Gain-Scheduling PI Control Based on Neural Networks

Stefania Tronci
Primo
;
Roberto Baratti
Secondo
2017-01-01

Abstract

This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.
GENERIC MODEL CONTROL; ACTIVATED-SLUDGE PROCESS; PREDICTIVE CONTROL; ADAPTIVE-CONTROL; CONTROL GMC; POLYMERIZATION; SYSTEMS; CSTR
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/233950
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