Auto-tuning PID controller using neural network

Pao Yue-kong Library Electronic Theses Database

Auto-tuning PID controller using neural network

 

Author: Bui, Tai-wah
Title: Auto-tuning PID controller using neural network
Degree: M.Sc.
Year: 1997
Subject: PID controllers
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: 1 v. (various pagings) : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1410597
URI: http://theses.lib.polyu.edu.hk/handle/200/2220
Abstract: PID auto-tuning controllers based on neural network model are described in this dissertation. There are a number of papers describing the auto-tuning PID controller using neural network. However, they are mainly realised in discrete time. Moreover, the neural network used directly identifies the process and is acted as a tool to determine the Jacobian of the process, hence to tune the parameters of the discrete time PID controller. Therefore, the neural network used requires a prior knowledge of the process such as the order and the deadtime of the process in order to have a good identification of the process. In this dissertation, a methodology is proposed which does not require a priori information on the process. In fact, the process is first on-line identified by a neural network accompanied with a first order plus deadtime (FPD) model generator based on the input and output response of the process. The parameters of the model are then used to tune a PID controller with reference to the ziegler and nichols tuning formula. The controller is realized by a continuous time implementation; i.e. a numerical solution of differential equation rather than a transformation to the Z-domain. Hence, the structure of the neural network does not depend on the order of the process. In addition, a PID auto-tuning controller with a single neuron structure is described. The single neuron used has three input connections that have associated weights. Each gain parameter corresponds to each weight of the single neuron. The performance of the PID controllers are simulated with three general types of process.

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