Design of Optimal Fractional Order PID Controller Using PSO Algorithm

Dastranj, Mohammad Reza; Rouhani, Mojtdaba; Hajipoor, Ahmad

doi:10.7763/ijcte.2012.v4.499

Cited by 24 publications

(9 citation statements)

References 4 publications

(2 reference statements)

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“…Matlab genetic algorithm toolbox is used to adjust the NPID controller parameters according to the earlier cost function. The population in each generation is represented by 80 x 7 population (P3) matrix as given by (8). Each row represents a one chromosome that include values and the last column is added to adapt fitness values (F) of corresponding chromosomes.…”

Section: Npid Controlmentioning

confidence: 99%

“…The fractional order PID controllers include two more parameters where the (λ) and (μ) are the power of (s) in integral and derivative actions respectively beside the three well known parameters proportional ( ), integral ( ) and derivative ( ) parameters [7]. These additional parameters increase the flexibility and robustness of this controller, and hence enhancing its dynamic performance compared to its integer counterpart [8], [9].…”

Section: Introductionmentioning

confidence: 99%

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Performance Study of Enhanced Non-Linear PID Control Applied on Brushless DC Motor

Shamseldin

Ghany²,

Mohamed

2018

IJPEDS

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<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-style: italic;">This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).</span>

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Section: Npid Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Study of Enhanced Non-Linear PID Control Applied on Brushless DC Motor

Shamseldin

Ghany²,

Mohamed

2018

IJPEDS

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“…An overview and brief description for particle swarm optimization along with algorithm features is given in [22][23][24][25][26][27][28]. This approach is appropriate to solve nonlinear problems and is established on flock activities such as birds detect food using gathering.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

Bharti

Saket

Nagar

2017

Eng. Technol. Appl. Sci. Res.

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This manuscript illustrates the controller design for a doubly fed induction generator based variable speed wind turbine by using a bioinspired scheme. This methodology is based on exploiting two proficient swarm intelligence based evolutionary soft computational procedures. The particle swarm optimization (PSO) and bacterial foraging optimization (BFO) techniques are employed to design the controller intended for small damping plant of the DFIG. Wind energy overview and DFIG operating principle along with the equivalent circuit model is adequately discussed in this paper. The controller design for DFIG based WECS using PSO and BFO are described comparatively in detail. The responses of the DFIG system regarding terminal voltage, current, active-reactive power, and DC-Link voltage have slightly improved with the evolutionary soft computational procedure. Lastly, the obtained output is equated with a standard technique for performance improvement of DFIG based wind energy conversion system.

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“…Genetic algorithm (GA) has the characteristics of a simple algorithm, parallel processing and global optimal solution (Tripp, 2010;Chen et al, 2013;Chorkawy and Etele, 2017). Both Mahdi (Mahdi, 2014) and Dastranj (Dastranj et al, 2011) used GA to set PID controller parameters and verified by experiment and simulation. In (Yi et al, 2016) aiming at the problem that traditional GA has low search efficiency and is easy to get into local optimal solution, an IGA is proposed.…”

Section: Introductionmentioning

confidence: 99%

The adaptive sliding mode control using improved genetic algorithm tuning PID controller for the planetary rover

Yuan

Guo

et al. 2021

AEAT

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Purpose The purpose of this paper is to resolve the problem of the dynamic response performance of the driving control system for a six-wheeled planetary rover. An adaptive sliding mode controller based on an improved genetic algorithm (IGA) to tune PID sliding surface parameters was used in the driving control system of the planetary rover. Design/methodology/approach First, the mathematical model of planetary rover driving control is established. Second, according to sliding mode variable structure control, an equivalent controller and a disturbance controller are constructed to solve the problem of a multi-disturbance nonlinear driving control system of planetary rovers and an IGA is used to tune PID parameters. Findings Simulation results show that the proposed control algorithm improves the accuracy of the driving control system and optimizes the smoothness of rover motion control. Practical implications The controller based on the IGA to tune PID sliding surface parameters has good self-adaptability and real-time controllability for the control object which is difficult to present a precise mathematical model. Originality/value The advanced control method is adopted to solve the uncertainty and external interference of planetary rovers in a complex environment. The mathematical model of the six-wheeled rover is established as the control object and the uncertainty and external disturbance of the model are considered. The controller based on IGA has good adaptability and real-time performance and the control algorithm can be used to drive robots in complex environments.

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Design of Optimal Fractional Order PID Controller Using PSO Algorithm

Cited by 24 publications

References 4 publications

Performance Study of Enhanced Non-Linear PID Control Applied on Brushless DC Motor

Performance Study of Enhanced Non-Linear PID Control Applied on Brushless DC Motor

Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

The adaptive sliding mode control using improved genetic algorithm tuning PID controller for the planetary rover

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