A new particle swarm optimization based auto-tuning of PID controller

Wang, Youbo; Peng, Xin; Wei, Benzheng

doi:10.1109/icmlc.2008.4620701

Cited by 28 publications

(20 citation statements)

References 5 publications

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“…Results are in Table.5 where minimu m HSI values using (18a) and performances of the controlled system are presented, the last row is for the variab le hunt rate in Equation (12).…”

Section: Dimpact Of Hunt Rate Values and Predators Number In Pmbbo 1mentioning

confidence: 99%

“…To study the influence of the hanut rate ρ over the PMBBO approach, several runs were carried out with the proposed variable hunt rate in (12) and d ifferent hunt rate from 0 to 1 with step of 0.1.…”

Section: Dimpact Of Hunt Rate Values and Predators Number In Pmbbo 1mentioning

confidence: 99%

“…The PID control involves three gains to be determined Proportional, Integral and Derivative [8]. Adjusting the PID parameters is considered as an optimization problem which has been solved by evolutionary algorith ms (EA s), including genetic algorith ms [9,10], ant colony optimization [11], particle swarm optimization [12,13] and biogeography based optimization (BBO) [14].…”

Section: Introductionmentioning

confidence: 99%

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Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

Salem¹,

Khelfi²

2014

IJISA

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Abstract-In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PM BBO) is presented. The approach uses several predators with new proposed prey's movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (M ass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.

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“…Results are in Table.5 where minimu m HSI values using (18a) and performances of the controlled system are presented, the last row is for the variab le hunt rate in Equation (12).…”

Section: Dimpact Of Hunt Rate Values and Predators Number In Pmbbo 1mentioning

confidence: 99%

Section: Dimpact Of Hunt Rate Values and Predators Number In Pmbbo 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

Salem¹,

Khelfi²

2014

IJISA

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show abstract

“…However, it does not provide an optimal tuning since it produces a high overshoot in the system response. To improve the performance of the conventional tuning methods, various intelligent methods have been presented [4][5][6][7][8][9][10][11][12][13][14]. These methods are based on Genetic Algorithm (GA), Iterative Feedback Tuning (IFT), Particle Swarm Optimization (PSO), and Fruit Fly Optimization Algorithm (FOA) techniques.…”

Section: Introductionmentioning

confidence: 99%

Genetic Tuned PID Controller Based Speed Control of DC Motor Drive

Deraz¹

2014

IJETT

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Abstract-Due to its extensive use for motion control systems in industry, tuning of the proportional-integral-derivative (PID) controller parameters has been the focus of intensive research. In this paper, a novel tuning method for the parameters of PID controller based speed control of DC motor using genetic algorithm (GA) is proposed. The main advantage of the proposed method is that the mathematical model of the system under control is not required, so it is useful in many industrial processes that have no obvious or complicated model. I. INTRODUCTIONHigh performance electrical motor drives are very important in industrial as well as other purpose applications. In general, a better performance of an electrical motor drive system has a better dynamic response. Among all motors, DC motors have been widely used in many adjustable speed drive applications that need high control requirements such as electric vehicles, steel rolling mills, electric cranes, high precision digital tools, and robotics. This is due to their simple, precise, wide range control characteristics. Speed of the DC motors is directly proportional to the armature voltage and inversely to the magnetic field flux. Controlling the armature voltage and/or the field current will adjust the DC motor speed.One of the most popular controllers has been used for speed control of the DC motors is the PID controller due to its simple structure, and good performance. In addition, with technology advancements, PID controller parameters can be easily changed without changing any hardware. However, the performance of the PID controller depends on the accuracy of the system model and parameters. In practice, the controlled systems are usually nonlinear and hence their accurate mathematical models are not available. In addition, the system parameters can vary with the time and the operating conditions. Therefore, the tuning methods of the controller parameters are of great importance. Tuning of the PID

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“…The focus of this work is pole arc optimization of SRM with the objective of maximizing average torque and minimizing torque ripple using PSO approach. The PSO Wang et al, 2008;Eberhart and Kennedy, 1995;Eberhart and Shi, 2000;Chaturvedi et al, 2009;Clerc and Kennedy, 2002) algorithm is one of the modern evolutionary algorithms. This algorithm was first proposed by Kennedy and Eberhart (1995).…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Approach for Optimal Design of Switched Reluctance Machine

Balaji¹

2011

American Journal of Applied Sciences

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Problem statement: Switched Reluctance Motors (SRMs) are widely used in various applications due to their inherent simplicity and rugged construction In SRM, torque output and torque ripple are sensitive to stator and rotor pole arcs and their selection is a vital part of SRM design process. In this study Particle Swarm Optimization technique is proposed for determining optimum pole arc of SRM. Approach: The problem of determining optimum pole arc is formulated as a multiobjective optimization problem with the objective of maximizing average torque and minimizing torque ripple. A comprehensive program based on analytical model is developed in Matlab to compute the value of inductance and average torque. Results: The optimization procedure is tested on 8/6, four-phase, 5 HP, 1500 rpm SRM. The results are compared and investigated with those obtained from Genetic Algorithm (GA) technique and Finite Element Analysis(FEA) simulation. Conclusion: The results demonstrate that the proposed method is effective and outperforms GA in terms of solution quality, accuracy, constraint handling.

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A new particle swarm optimization based auto-tuning of PID controller

Cited by 28 publications

References 5 publications

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems

Genetic Tuned PID Controller Based Speed Control of DC Motor Drive

Particle Swarm Optimization Approach for Optimal Design of Switched Reluctance Machine

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