自动化专业毕业论文外文文献翻译

目录

Part 1 PID type fuzzy controller and parameters

adaptive method (1)

Part 2 Application of self adaptation fuzzy-PID

control for main steam

temperature control system in power station错误!未定义书签Part 3 Neuro-fuzzy generalized predictive

control of boiler steam temperature .... (22)

Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义

预测控制37

Part 1 PID type fuzzy controller

and Parameters adaptive method

Wu zhi QIAO, Masaharu Mizumoto

Abstract: The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures.

Keywords: Fuzzy controller; PID control; Adaptive

control

1. Introduction

Among various inference methods used in the fuzzy controller found in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Min-max compositional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the product-sum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Min-max compositional rule of inference.

In the algorithm of a fuzzy controller, the fuzzy function calculation is also a complicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With

this model of crisp real number output, the fuzzy set of the inference consequence will be a discrete fuzzy set with

a finite number of points, this can greatly simplify the fuzzy function algorithm.

Both the Min-max method and the product-sum method are often applied with the crisp output model in a mixed manner. Especially the mixed product-sum crisp model has a fine performance and the simplest algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the product-sum crisp type fuzzy controller.

2. PID type fuzzy controller structure

As illustrated in previous sections, the PD function approximately behaves like a parameter time-varying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steady-state error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in