PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Zhong, Yuanchang; Huang, Xu; Meng, Pu; Li, Fachuan

doi:10.1155/2014/731368

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…They applied the proposed method to an intellectual challenge in robotics, that is, a biped robot walking in the lateral plane on slope. Zhong et al [246] presented an improved PID intelligent control algorithm, which was applied to the electric gas pressure regulator. The algorithm used the improved radial basis function (RBF) neural network based on PSO algorithm to make online adjustment on PID parameters.…”

Section: Automatic Controlmentioning

confidence: 99%

“…Ganguly et al [221], Komsiyah [222], Feng et al [223], Pekşen et al [224], Yang et al [225], de Mendonça et al [226], Liu et al [227], Aich and Banerjee [228], Chou et al [229], Lee et al [230], Thakral and Bakhshi [231], Fister et al [232], Aghaei et al [233], Selakov et al [234], Shirvany et al [235], and Tungadio et al [236] Automatic control Cai and Yang [237], Kolomvatsos and Hadjieftymiades [238], Pandey et al [239],Štimac et al [240], Nedic et al [241], Chang and Chen [242], Xiang et al [243], Danapalasingam [244], Mahmoodabadi et al [245], Zhong et al [246], Perng et al [247], Huang and Li [248], and Nisha and Pillai [249] Communication Yousefi et al [250], Sun et al [251], Yongqiang et al [252], Chiu et al [253], Zubair and Moinuddin [255], Kim and Lee [256], Yazgan and Hakki Cavdar [257], Rabady and Ababneh [258], Das et al [259], Scott-Hayward and Garcia-Palacios [260], Omidvar and Mohammadi [261], and Kuila and Jana [262] Operations Liu and Wang…”

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Automatic Controlmentioning

confidence: 99%

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

698

380

View full text Add to dashboard Cite

show abstract

“…In the PSO algorithm, a particle corresponds to a feasible solution. First, we code the particle, which includes the center value and width of the basis function, particle velocity, and fitness [21]. According to the subtractive clustering algorithm, suppose m centers are determined and each center is k-dimensional, then the position of the particle is m × ðk + 1Þ-dimensional, the velocity of it is also m × ðk + 1Þ-dimensional, σ i represents the width of the i basis function, and f i is the fitness of the i individual.…”

Section: Implementation Of Improved Pso In the Rbfmentioning

confidence: 99%

RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

Zhu

Wang

Zheng

et al. 2021

Wireless Communications and Mobile Computing

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On the basis of the chaotic features of the frequency hopping signal, frequency band prediction for frequency hopping signal can enhance the interference effect of the signal greatly. However, poor prediction accuracy often limits its development in the military field. Therefore, for the sake of enhancing the frequency band prediction accuracy of frequency hopping signal, this paper studies the radial basis function (RBF) neural network frequency hopping signal frequency band prediction model based on the gradient descent method and improved the particle swarm optimization algorithm, respectively. The former uses a step-by-step algorithm to optimize the center value and weight so that the network can find the most suitable initial state. Then, the clustering selection optimization algorithm is employed to optimize the central value. In addition, it optimizes the weight by using a gradient descent method of the optimal learning rate. The latter optimizes the structure of the RBF neural network through the combination of the subtractive clustering algorithm and improved the particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the gradient RBF algorithm model performs better in terms of accuracy, but time efficiency is lower, while the PSO-RBF algorithm has better time efficiency.

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“…Moreover, with conventional PID control systems, previous literatures have proposed alternate approaches for controlling the temperature and humidity of the drying chamber [11][12][13]. The experts fixed conventional PID controller parameters cannot be changed online, which leads to more trouble to attain the expected quality [14]. The conventional PID controller is required to additional advance in the performance to shorten steady state error, peak over shoot and settling time.…”

Section: Introductionmentioning

confidence: 99%

PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System

Krishnan¹,

R²,

Nishanth³

et al. 2018

EAI Endorsed Transactions on Energy Web

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INTRODUCTION: Preservation of fruits by drying is one of the general and important traditional technique followed by the process industries. An accurate controller of relative humidity and temperature is required for the fruit drying control system, which determines the quality of the dried fruits. OBJECTIVES: To design optimal Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) for pineapple drying system. METHODS: A Propositional-Integral-Derivative (PID) controller based on the Particle Swarm Optimization and Radial Basis Functional Neural Network (PSO-RBFNN) was proposed in this paper for pineapple drying system. Also, the coupling relationship of relative humidity and temperature is more complicated due to the fluctuations and non-linearity in the drying system. An intelligent Adaptive Neuro Fuzzy Inference System (ANFIS) coupling model is utilized in this paper to access the coupling relationship between relative humidity and temperature. RESULTS: The proposed control system has been implemented in the MATLAB and results are compared with PID controller, Fuzzy Logic Controller (FLC) and Fuzzy PID controller for the performance constraints such as settling time, peak over shoot and steady state error. CONCLUSION: The proposed PSO-RBFNN based PID controller gives better control performance with the highly minimized settling time (42 sec for humidity and 40 sec for temperature) and completely eliminated steady state error and Peak overshoot. Finally, the PSO-RBFNN algorithm based PID controller is concluded as the effective system.

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PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Cited by 6 publications

References 16 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

RBF Neural Network‐Based Frequency Band Prediction for Future Frequency Hopping Communications

PSO-RBFNN Based Optimal PID Controller and ANFIS Based Coupling for Fruits Drying System

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