PID-type fuzzy logic controller tuning based on particle swarm optimization

Bouallègue, Soufiene; Haggège, Joseph; Ayadi, Mounir; Benrejeb, Mohamed

doi:10.1016/j.engappai.2011.09.018

Cited by 121 publications

(76 citation statements)

References 14 publications

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“…To overcome these shortages, various methods were proposed. In 1999, Shi and Eberhart proposed the linear inertia factor to improve the exploration and exploitation capacities of the PSO algorithm [41]. In [42], an improved particle swarm optimization based on the D-cent chaotic model was proposed to improve the convergence rate and reduce the iterative time.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

Wang

Tan

et al. 2017

Algorithms

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Abstract:In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

Wang

Tan

et al. 2017

Algorithms

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“…Unlike the GA, the PSO has no evolution operators such as crossover and mutation. The PSO is less prone to getting trapped in local minima and has good computational efficiency [19]. The PSO starts with an initial population of randomly generated solutions called particles which fly through the search space.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Comparison of PID based Control Algorithms for Daily Blood Glucose Control

Soylu¹,

Danışman²

2016

World Congress on Electrical Engineering and Computer Systems and Science

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-Type 1 Diabetes Mellitus (T1DM) is a worldwide disease. Although a complete cure has not been found yet, an artificial pancreas (AP), also known as a closed-loop insulin therapy, is becoming more important for the treatment of this disease. Controller part of the AP can compute insulin infusion rate that will keep blood glucose concentration (BGC) in normoglycemic ranges for patients with T1DM. In this paper, three different control algorithms are proposed as a controller part of the AP. These control algorithms include genetic algorithm based proportional-integral-derivative (GA-PID) control, artificial bee colony algorithm based PID (ABC-PID) control, and particle swarm optimization algorithm based PID (PSO-PID) control. In silico control studies are implemented through a virtual diabetic patient based on the Stolwijk-Hardy's glucose-insulin regulation model. Simulations are performed to assess control function in terms of tracking BGC profile of a healthy person against to a daily food intake of three meals. In order to demonstrate robustness, sensor noise test is implemented. Simulation results are promising in terms of regulating the daily BGC.

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“…In this study, the inertia weight is linearly decreased from = 0.9 to = 0.4. The swarm size can be determined according to complexity of problems so that there is a suggestion to choose swarm size between 20 and 50 in the most studies [21,22].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Performance of PSO Based Classical and Intelligent Controllers for Water Level Control of a Steam Generator

Altaş¹,

Ozkop²

2017

deufmd

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Abstract:In this paper, different controller techniques based on particle swarm optimization (PSO) algorithm are proposed to control the water level of a steam generator with multiple inputmultiple output (MIMO) characteristics. The techniques employed are classical proportional+integral+derivative (PID) control, fuzzy logic control (FLC) and fuzzy tuned proportional-integral control (FTPIC). Gains of PID controller and parameters of FLC (the core and the boundaries of triangular membership functions in input and output spaces) are optimized by the PSO. Validations of the proposed PSO based PID control (PSO-PID), PSO based fuzzy logic control (PSO-FLC) and PSO based fuzzy tuned PI control (PSO-FTPIC) techniques are done with numerical simulation in using MATLAB. The simulation results show that the PSO-PID provides better performance for controlling the water level of a steam generator compared to the others. Buhar Generatörünün Su Seviyesi Denetimi için PSO Temelli Klasik veAkıllı Denetleyicilerin Performansı Keywords Denetim, Bulanık Mantık, Parçacık Sürü, Optimizasyon, ÇGÇÇ Özet: Bu çalışmada çok giriş-çok çıkış (ÇGÇÇ) özelliğine sahip buhar generatörünün su seviyesi denetimi için parçacık sürü optimizasyonu (PSO) algoritmasına dayanan farklı kontrol teknikleri önerilmektedir. Bu teknikler, klasik oransal-integraltürevsel (PID) denetim, bulanık mantık denetim (BMD) ve bulanık ayarlı oransal-integral denetimdir. PID denetleyicilerin kazançları ve BMD'nin parametreleri (giriş ve çıkıştaki üçgen üyelik fonksiyonların merkezleri ve sınırları) PSO tarafından en uygun hale getirilmektedir. Önerilen PSO temelli PID denetim (PSO-PID), PSO temelli bulanık mantık denetim (PSO-BMD) ve PSO temelli bulanık ayarlı PI denetim (PSO-BAPI) tekniklerinin gerçeklemesi, MATLAB kullanılarak sayısal benzetim ile doğrulanmaktadır. Benzetim sonuçları, buhar generatörünün su seviyesi denetim için PSO-PID tekniğini diğerlerine göre daha iyi performans sergilediğini göstermektedir.

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PID-type fuzzy logic controller tuning based on particle swarm optimization

Cited by 121 publications

References 14 publications

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

Comparison of PID based Control Algorithms for Daily Blood Glucose Control

Performance of PSO Based Classical and Intelligent Controllers for Water Level Control of a Steam Generator

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