PID控制器调校技术综述

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"这篇论文是Hari Om Bansal、Rajamayyoor Sharma和P.R. Shreeraman合著的《PID控制器调参技术综述》，发表在2012年《控制工程与技术》(JCET)杂志第2卷第4期，主要探讨了PID控制器的调参方法，包括古典技术和优化技术，并提供了部分技术的对比分析，旨在为从事PID控制器工作的人提供全面的参考资源。关键词包括PID控制器、调参、古典技术、智能计算技术。" PID控制器调参技术是工业控制领域中的核心问题，因为PID控制器长期以来一直是工业过程控制的首选。这些控制器结合了比例（P）、积分（I）和微分（D）三个元素，以实现对系统响应的精确控制。调参是确保控制器性能的关键步骤，它涉及到调整PID参数以达到最佳的系统响应。论文首先回顾了PID控制器的古典调参技术。这些技术通常基于经验法则，如Ziegler-Nichols规则，它通过阶跃响应试验来确定初始参数，然后通过试错法进行微调。另外，Smith预估器等技术也被纳入古典调参方法，它考虑了系统的滞后特性，提高了控制性能。其次，文章讨论了用于PID调参的优化技术。这包括数学优化方法，如根轨迹法和频率域法，它们利用系统频率响应特性来确定最优参数。还有基于现代控制理论的方法，如自适应控制和模型预测控制，这些方法允许控制器自动适应系统变化，提高鲁棒性。此外，论文还比较了一些调参技术，比如基于智能计算的方法，如模糊逻辑、神经网络和遗传算法。这些方法利用人工智能的特性，能够处理非线性和复杂系统的调参问题，往往比传统方法更具灵活性和效率。这篇综述论文为读者提供了一个关于PID控制器调参技术的全面视图，涵盖了从基础的古典方法到先进的智能计算方法的广泛范围，对于理解PID控制器调参的现状和未来发展方向具有重要价值。对于设计和实施PID控制系统的工程师和研究人员来说，这是一个宝贵的参考资料。

Journal of Control Engineering and Technology (JCET)

JCET Vol. 2 Iss. 4 October 2012 PP. 168-176 www.ijcet.org

○

C World Academic Publishing

PID Controller Tuning Techniques: A Review

Hari Om Bansal, Rajamayyoor Sharma, P. R. Shreeraman

Electrical and Electronics Engineering Department, Birla Institute of Technology and Science

Pilani, India

hobansal@gmail.com

Abstract- This paper presents a review of the current as well as

classical techniques used for PID tuning. PID controllers have

been used for industrial processes for long, and PID tuning has

been a field of active research for a long time. The techniques

reviewed are classified into classical techniques developed for

PID tuning and optimization techniques applied for tuning

purposes. A comparison between some of the techniques has

also been provided. The main goal of this paper is to provide a

comprehensive reference source for people working in PID

controllers.

Keywords- PID Controllers; Tuning; Classical Techniques;

Intelligent Computational Techniques

I. INTRODUCTION

Proportional Integral and Derivative (PID) controllers

have been used in industrial control applications for a long

time. PID controllers date to 1890s governor design

[1]-[2]

Despite having been around for a long time, majority of

industrial applications still use PID controllers. According

to a survey in 1989, 90% of process industries use them

[3]

This widespread use of PID in industry can be attributed to

their simplicity and ease of re-tuning on-line

[4]

The PID controller is so named because its output sum

of three terms, proportional, integral and derivative term.

Each of these terms is dependent on the error value e

between the input and the output,

output =

dte(t)

Ke(t)

K ×+

∫

×+×

(1)

where K

, K

and K

are the P, I and D parameters

respectively. K

and K

can also be written as,

pdip

KK,TK

K ×=×=

(2)

where T

and T

are reset time and derivative time

respectively. These terms determine the type of system

response. The properties of P, I and D are discussed briefly

here.

Proportional term: This term speeds up the response as

the closed loop time constant decreases with the

proportional term but does not change the order of the

system as the output is just proportional to the input. The

proportional term minimizes but does not eliminate the

steady state error, or offset.

Integral term: This term eliminates the offset as it

increases the type and order of the system by 1. This term

also increases the system response speed but at the cost of

sustained oscillations.

Derivative term: This term primarily reduces the

oscillatory response of the system. It neither changes the

type and order of the system nor affects the offset.

A change in the proportionality constants of these terms

changes the type of response of the system. That is why PID

tuning, which is the variation of the PID proportionality

constants, is of utmost importance. This paper talks about

the different types of PID tuning techniques implemented

and the comparison between some of them.

There have been various types of techniques applied for

PID tuning, one of the earliest being the Ziegler Nichols

technique. These techniques can be broadly classified as

classical and computational or optimization techniques.

A. Classical Techniques

Classical techniques make certain assumptions about the

plant and the desired output and try to obtain analytically, or

graphically some feature of the process that is then used to

decide the controller settings. These techniques are

computationally very fast and simple to implement, and are

good as a first iteration. But due to the assumptions made,

the controller settings usually do not give the desired results

directly and further tuning is required. A few classical

techniques have been reviewed in this paper.

B. Computational or Optimization Techniques

These are techniques which are usually used for data

modeling and optimization of a cost function, and have been

used in PID tuning. Few examples are neural networks

(computational models to simulate complex systems),

genetic algorithm and differential evolution. The

optimization techniques require a cost function they try to

minimize. There are four types of cost functions used

commonly,

• Integral Absolute Error

IAE=

(3)

• Integral Square error

ISE=

(4)

• Integral Time Absolute Error

ITAE=

(5)

• Integral Time square Error

ITSE=

(6)

Computational models are used for self tuning or auto

tuning of PID controllers. Self tuning of PID controllers

essentially sets the PID parameters and also models the

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