An Enhanced PID Control Strategy for Unstable Processes

Park, Jin Hyun; Sung, Su Whan; Lee, In‐Beum

doi:10.1016/s0005-1098(97)00235-5

Cited by 112 publications

(62 citation statements)

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“…1 (see Jacob & Chidambaram, 1996;Park et al, 1998, Paraskevopoulos et al, 2004. As it can easily verified, the loop transfer functions obtained by these control schemes are identical, provided that the following relations hold …”

Section: Pid-like Controller Structures For Usopdt Processesmentioning

confidence: 75%

PID-Like Controller Tuning for Second-Order Unstable Dead-Time Processes

Pasgianos¹,

Arvanitis²,

Boglou³

2012

Introduction to PID Controllers - Theory, Tuning and Application to Frontier Areas

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Section: Pid-like Controller Structures For Usopdt Processesmentioning

confidence: 75%

PID-Like Controller Tuning for Second-Order Unstable Dead-Time Processes

Pasgianos¹,

Arvanitis²,

Boglou³

2012

Introduction to PID Controllers - Theory, Tuning and Application to Frontier Areas

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“…Search procedures that mimic with natural evolution of the species IN a natural system are coined as Evolutionary strategies [6]. Like GA, they require data based on objective function and constraints, and not the derivatives or other auxiliary knowledge.…”

Section: A Evolution Strategymentioning

confidence: 99%

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Anandaraju¹,

Puttaswamy²

2014

IJAREEIE

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Proportional-Integral-Derivative (PID) Controllers plays a significant role in many industrial and commercial applications. Designing of PID controller is always a challenging problem especially for high order systems. It is known that PID Controller is employed in every aspect of industrial automation. The PID Controller applications are spanned from small industry to higher technology industries. Hence, the problem is always how to optimize the PID Controller. Generally, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This simulation involves the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating range. Self-tuning method for PID Controller using Evolutionary Computational (EC) Techniques capable of providing robust design. In this paper two types of tuning methods are presented, namely Genetic Algorithm and Modified Interactive Evolutionary Computation Method. The results obtained by them based on velocity control of DC motor and harmonic analysis are analysed and a comparative study of the two are presented. KEYWORDS: DC motor, PID, Genetic Algorithm, MIEC I.INTRODUCTIONPID Controller is a generic control loop feedback mechanism and is widely used in control system. PID Controllers use three basic behavior types or modes: P -proportional, I -integral and D -derivative. While proportional and integral are together used as single control mode, a derivative mode is rarely used on its own in control systems. Combinations such as PI and PD control are very often used in practical systems. A proportional-integral-derivative controller or commonly known as PID Controller is a generic control loop feedback mechanism (controller) widely used in industrial control systems. Therefore, a PID is the most commonly used feedback controller [1]. Such type of controller estimates fault rate as the difference between a considered method variable and a required set-point. The designed controller tries to reduce the error by correcting the process control inputs. The PID Controller algorithm consists of three distinct stable parameters, and is consequently termed as three-term control, they are proportional, integral and derivative values, denoted as P, I, and D. The weighed sum of these three events is deployed to regulate the process using a control constituent. A PID Controller is the most excellent controller. The controller has a potential to facilitate the organized act programmed for specific process needed by modifying the three parameters in a PID Controller algorithm. The block diagram of a PID Controller is shown in Fig. 1

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“…There are many sources focused on the control system design for unstable processes, e.g. [1], [11], [12], [13], [2], [14], [15]. Many of these works solve the control system design problem connected also with delayed and non-minimum-phase systems which are also problematic to control.…”

Section: Stabilizationmentioning

confidence: 99%

Unstable Systems Database: A New Tool for Students, Teachers and Scientists

Gazdoŝ

Marholt

Kolařík

2013

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

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Abstract. The contribution presents a starting project of a site focused on unstable systems. It is a web-based database in the bilingual version (ENG/CZ) which can be used as an information database for models of unstable processes. The site contains mathematical models of such systems including their simulation files together with basic information about stability of dynamical systems. The paper outlines motivation for development of this database, presents its basic structure and discusses several models from the site. Areas of prospective usage are also suggested together with possible directions of further development of this project.

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An Enhanced PID Control Strategy for Unstable Processes

Cited by 112 publications

References 0 publications

PID-Like Controller Tuning for Second-Order Unstable Dead-Time Processes

PID-Like Controller Tuning for Second-Order Unstable Dead-Time Processes

Comparative Studies on Evolutionary Computational Techniques for Speed Control of A DC Motor

Unstable Systems Database: A New Tool for Students, Teachers and Scientists

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