A neural network PI controller tuner

Chan, Kenny L.; Leong, SS; Lin, Gaorong

doi:10.1016/0954-1810(94)00014-v

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…In a neural PID controller, the output of the neural network usually corresponds to the parameters of the controller. A variety of neural networks, such as multivariable neural networks trained by back propagation and receptive field neural networks, have been used for determining the parameters of PID controllers .…”

Section: Introductionmentioning

confidence: 99%

Parameters self‐tuning PID controller circuit with memristors

Liang

Huang

2017

Circuit Theory & Apps

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SUMMARYTo achieve the satisfactory control performance of a dynamic system using a proportion-integrationdifferentiation (PID) controller, it is necessary that the three parameters of the PID controller are set to values that suit the system to be controlled. Most current applications employ fixed setting parameters that are not adequate for nonlinear time varying systems with uncertainty and disturbances. In this paper, we use neuron weighted tuning functions to develop self-tuning methods that can automatically tune the parameters of PID controllers. The weighting processes performed using voltage-controlled memristors and current-controlled memristor are discussed. It is proven that the output tracking error of a closed-loop system controlled by the proposed controllers and the weight update rules governed by the memristors descends toward its minimum. A simulation and experimental verification of the performances of these controllers in linear and nonlinear systems are included. This proposed PID controllers are shown to be more robust than conventional PID controllers.

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Section: Introductionmentioning

confidence: 99%

Parameters self‐tuning PID controller circuit with memristors

Liang

Huang

2017

Circuit Theory & Apps

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“…Like the two ways to build ANN controllers, there are two ways to make the connection. One method is to adjust the PID parameters by ANN ( [3], [4]); the other method is to create the ANN based on the systems output error signal ( [5], [6], [7]). The first method involves emulating the thoughts of an expert control engineer by tweaking the tuning parameters according to the empirical rules.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear PID controller for CSTR using local model networks

Gao¹,

O'dywer²,

Coyle³

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527)

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-The basic PID controllers have difficulty in dealing with problems that appear in complex non-linear processes. This paper presents a practical non-linear PID controller that deals with these non-linear difficulties. It utilises a local model (LM) network, which combines a set of local models within an artificial neural network (ANN) structure, to adaptively characterise the process nonlinearity. Then a local controller network is formulated through a gating system deduced from the LMN to handle the non-linearity. A continuous stirred tank reaction (CSTR) case study illustrates the practicality of this method in the modelling and control of non-linear processes. PID controllers are still alive and appropriate for the control of non-linear processes.

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Efficient Control Scheme for Surface Temperature of Hot Roller Based on Neural Network

Hou

2012

Communications in Computer and Information Science

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A neural network PI controller tuner

Cited by 10 publications

References 4 publications

Parameters self‐tuning PID controller circuit with memristors

Parameters self‐tuning PID controller circuit with memristors

A nonlinear PID controller for CSTR using local model networks

Efficient Control Scheme for Surface Temperature of Hot Roller Based on Neural Network

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