Application of Particle Swarm Optimization Algorithm on Robust PID Controller Tuning

Zhao, Jun; Tianpeng, Li; Qian, Jixin

doi:10.1007/11539902_118

Cited by 53 publications

(34 citation statements)

References 10 publications

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“…The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The use of PSO has been reported in many of the recent works [16]in this field. PSO has been regarded as a promising optimization algorithm due to its simplicity, low computational cost and good performance [17].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

PSO Based Tuning of a PID Controller for a High Performance Drilling Machine

Kumar¹,

Jayaraj²,

Kishan³

2010

IJCA

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The paper deals with optimal tuning of a Proportional-IntegralDerivative (PID) controller used in a high performance drilling system for controlling the output obtained and hence to minimize the integral of absolute errors (IAE). The main objective is to obtain a stable, robust and controlled system by tuning the PID controller using Particle Swarm Optimization (PSO) algorithm. The incurred value is compared with the traditional tuning techniques like Ziegler-Nichols and is proved better. Hence the results establishes that tuning the PID controller using PSO technique gives less overshoot, system is less sluggish and reduces the IAE.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

PSO Based Tuning of a PID Controller for a High Performance Drilling Machine

Kumar¹,

Jayaraj²,

Kishan³

2010

IJCA

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“…In optimal PID tuning, typical performance criteria have been used to evaluate closed-loop system response include the Integral Square-Error (ISE) index, Integral-of-Time multiplied by Square-Error (ITSE) index, Integral Absolute-Error (IAE) index, and Integral-of-Time multiplied by Absolute-Error (ITAE) index [7]. Each of them has its own characteristic performance.…”

Section: Performance Criteriamentioning

confidence: 99%

“…It can be used in different forms such as stand alone, or in combination with logic control systems, to make complicated automatic systems used for energy and power production, manufacturing, and latter used for oil refining plants [5]. Its widespread use is attributed to its simple structure and robust performance over a wide range of operating conditions [6,7]. Therefore, an extensive study on the optimization of the controller parameters is necessary.…”

Section: Pid Controllermentioning

confidence: 99%

Application of particle swarm optimization approach to improve PID performance for a gas turbine temperature control system

Marzoughi

Selamat

Marzoughi

2010

2010 IEEE Student Conference on Research and Development (SCOReD)

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Classical tuning methods of Proportional, Integral, and Derivative (PID) controller, such as the Ziegler-Nichols and Cohen-Coon tuning formula are able to tune the PID controller easily, when used in controlling linear systems. However, experimental studies have shown that these conventional methods cause unsatisfactory performance when controlling nonlinear processes. In this paper, particle swarm optimization (PSO) algorithm is used in optimizing the PID controller parameters for the exhaust temperature control of a gas turbine system. Compared with the Ziegler-Nichols method, the proposed method is more effective in improving the step response characteristic, such as absolute error, rise time, and settling time.

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“…[12] The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The use of PSO has been reported in many of the recent works; [13] PSO has been regarded a promising optimization algorithm due to its simplicity, low computational cost, and good performance. [14] The model of the process under study is very important for its tuning as the accuracy of the tuned controller parameters is greatly dependent upon the degree of accuracy of the system model with that of the real system.…”

Section: Particle Swarm Optimization Technique Basedmentioning

confidence: 99%

Particle Swarm Optimization Technique Based Design of Pi Controller for a Real-Time Non-Linear Process

Kumar

Sivasankar

Radhakrishnan

et al. 2008

Instrumentation Science & Technology

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The increasing complexity of modern control systems has emphasized the idea of applying new approaches in order to solve design problems for different control engineering applications. Proportional-Integral-Derivative (PID) control schemes have been widely used in most of process control systems represented by chemical processes for a long time. However, a very important problem is how to determine or tune the PID parameters, because these parameters have a great influence on the stability and the performance of the control system. Computational intelligence (CI), which has caught the eyes of researchers due to its simplicity, low computational cost, and good performance, makes it a possible choice for tuning of PID controllers, to increase their performance. This paper discusses, in detail, the Particle Swarm Optimization (PSO) algorithm, a CI technique, and its implementation in PID tuning for a controller of a real time process. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the proposed method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

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Application of Particle Swarm Optimization Algorithm on Robust PID Controller Tuning

Cited by 53 publications

References 10 publications

PSO Based Tuning of a PID Controller for a High Performance Drilling Machine

PSO Based Tuning of a PID Controller for a High Performance Drilling Machine

Application of particle swarm optimization approach to improve PID performance for a gas turbine temperature control system

Particle Swarm Optimization Technique Based Design of Pi Controller for a Real-Time Non-Linear Process

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