Advanced particle swarm optimization-based PID controller parameters tuning

Jalilvand, Abolfazl; Kimiyaghalam, A.; Ashouri, Ahmad; Mahdavi, Meisam

doi:10.1109/inmic.2008.4777776

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…The PID controller design is the simplest controller in terms of easy installation and robust performance. 17 To provide better closed-loop performance of the controller, three parameters of the PID should be tuned according to the boundary conditions. To obtain suitable PID parameters, a tuner-based PID (TBPID) controller design is presented using MATLAB/Simulink/Control System Tuner toolbox.…”

Section: Controller Design and Resultsmentioning

confidence: 99%

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Active vibration control of a smart beam by a tuner-based PID controller

Gülbahçe

Çeli̇k

2018

Journal of Low Frequency Noise, Vibration and Active Control

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In this study, a tuner-based Proportional-Integral-Derivative controller is proposed to actively control a smart beam. In numerical simulation environment, the performance of the tuner-based PID and a positive position feedback controller in damping the forced vibrations of a smart beam using a piezoelectric actuator are investigated. The finite element method is used to numerically model the smart beam by exporting the state-space matrices that are characterized with regard to the active vibration control loop. Two types of vibration data with sine tones are comprised in order to stimulate behavior of the proposed system. The first one is the composition of the first and second natural frequencies of smart beam. The second one is the composition of the first to the third natural frequencies of smart beam. In the tuner-based PID, controller design tuner toolbox is used to obtain suitable PID coefficients. In this simulation environment active vibration control based on the proposed tuner-based PID and on positive position feedback controllers is studied and compared. Additionally, the controller power consumption levels are determined for the proposed controller design. Numerical results show that the overall tuner-based PID control performance of flexible smart beam system is more effective than the positive position feedback controlled system for forced vibration control. Also, the tuner-based PID controller provides more energy savings than the positive position feedback controller.

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Section: Controller Design and Resultsmentioning

confidence: 99%

“…Then, the control performances of these methods are tested by using different evaluation methods. 13–18…”

Section: Introductionmentioning

confidence: 99%

Active vibration control of a smart beam by a tuner-based PID controller

Gülbahçe

Çeli̇k

2018

Journal of Low Frequency Noise, Vibration and Active Control

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“…It can be seen that the PSO algorith m by using the concave function decreasing inertia weight has been accelerated, the inert ia weight is reduced fast in the init ial stage of the algorith m, and the convergence speed of the algorith m is accelerated, the optimization accuracy is the highest. The PSO algorith m is used for PID parameter tuning, according to the PSO algorithm tuning principle and the different ways of implementation, the tuning methods can be divided into off-line PSO-PID and on-line PSO-PID [13][14][15][16][17][18]. Figure 4 is the PSO-PID control system structure diagram.…”

Section: Fig2 Optimization Comparison Between Ga and Psomentioning

confidence: 99%

Research on SVG DC-Side Voltage Control Based-on PSO Algorithm

Xiao¹

2016

IJITCS

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The operation flow of particle swarm optimization (PSO) is presented, at the same time the PSO algorithm and GA algorith m are used to find the optimal value of the standard function, simu lation results show that the PSO algorith m has better global search performance and faster search efficiency. The inertia weight decreasing strategy of PSO algorith m is studied, the simulation results show that the concave function decreasing strategy can accelerate the convergence rate of the algorithm. The stability control of the DC-side voltage is very important for the static var generator (SVG) co mpensation, but the disadvantages of the traditional PI control are fixed parameters and poor adaptability of dynamic response, PSO algorith m is introduced to the optimizat ion of PI parameters, so online PSO-PI control and off-line PSO-PI control are obtained, the SVG voltage loop transfer function is used as the controlled object. The simulat ion results show that the PSO-PI control can satisfy the time varying system of the controlled object with strong adaptability.

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“…With this concept, a new strategy is employed to improve the performance of the original bare bone particle swarm optimization. Instead of using only one best particle drawn from the neighborhood, the strategy used here is to combine two best particles taken respectively from the whole swarm and a ring structure neighborhood, i.e., the mean and standard deviation of the Gaussian distribution are now modified to ) p 0.5(p μ id md + = (7) id md p p ı − = (8) where p m is a weighted mean of global best and best of a small social neighborhood. Two variants are possible, as shown in Eq.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Particle swarm optimization (PSO) [1][2][3], inspired from the bird flocking and fish schooling, is a meta-heuristic, population-based optimization method that has been applied successfully to diverse engineering problems, for examples, economic dispatching and optimal power flow in power system [4,5], image enhancement [6], parameter tuning for PID controller [7], power filter design [8], robot application [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Bare bone particle swarm optimization with integration of global and local learning strategies

Chen

2011

2011 International Conference on Machine Learning and Cybernetics

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Bare bone particle swarm optimization (BPSO) possesses self-adapting property and uses fewer parameters resulted in simple implementation and free parameter-tuning. Inevitably, it also tends to converges prematurely, especially for problems with multiple extremes. In this paper, a new method combining global and local learning strategy used in traditional particle swarm optimization (PSO) is devised to improve the performance of the bare bone particle swarm optimization. According to the integration, two variants are proposed. Method is simple and the results are fruitful. Tested on a suite of benchmark functions, unimodal and multimodal functions, justifies the feasibility of the strategy. Both solution quality and convergent speed are better than traditional bare bone particle swarm optimizer.

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Advanced particle swarm optimization-based PID controller parameters tuning

Cited by 18 publications

References 8 publications

Active vibration control of a smart beam by a tuner-based PID controller

Active vibration control of a smart beam by a tuner-based PID controller

Research on SVG DC-Side Voltage Control Based-on PSO Algorithm

Bare bone particle swarm optimization with integration of global and local learning strategies

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