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Enhancement neural control scheme performance using pso adaptive rate: Experimentation on a transesterification reactor

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JOURNAL OF VIBRATION AND CONTROL 2023年第15-16期29卷 3769-3782页

作者： Yassin, Farhat Ali, Zribi Asma, Atig Ridha, Ben Abdennour Univ Gabes Natl Engn Sch Gabes Res Lab Numer Control Ind Proc Gabes Tunisia

An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and controller with decoupled adaptive rates. Constraints on the adaptive rate are derived from the Lyapunov stability method. Particle Swarm Optimization is proposed as a mechanism to optimize the adaptive rate of the NC to improve the closed-loop performances. The advantages of the proposed new control algorithm are as follows: (1) online optimal choice of adaptive rate, which reduces the effort for searching an adequate neural controller adaptive rate when considering the conventional methods and (2) ensuring stability, faster convergence, disturbance rejection, and good tracking. The efficiency of the proposed pso adaptive rate is demonstrated with numerical control of SISO nonlinear system. The obtained results prove the efficiency of the proposed NC compared to those obtained with existing methods. An application of the developed approach on a semi-batch reactor is presented to validate simulations results.

关键词： Nonlinear systems adaptive neural control lyapunov stability pso algorithm chemical reactor

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PM2.5 Measuring Method Using RBF Neural Network Combined With pso algorithm

PM2.5 Measuring Method Using RBF Neural Network Combined Wit...

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第27届中国控制与决策会议

作者： XU Lin GUAN Tian-yi LIYan-nong ZHENG Wen-jing GUO Jing-yi College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479970186

Considering the disadvantage of PM2.5 detection devices,there are many problems such as the low automation,poor test repeatability,and dielectric material loss *** paper presents a new PM2.5 detection system using laser diffraction technique based Fraunhefor diffraction *** Radial Basis Function(RBF) neural network with the inputs of multiple laser diffraction signals is used to be the micro particles calculating model to improve the detecting *** tackle the problems in the training algorithms,the Particle Swarm Optimization(pso)algorithm is employed to optimize the key parameters of the RBF neural network(RBFNN).The simulation and experiment results show that the new PM2.5 detection system satisfies the detection requirements with the high calculating precision,and effectively overcomes the problems in the conventional detection system.

关键词： PM25 detection Laser diffraction RBF neural network pso algorithm

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Research of MPPT Control Method Based on pso algorithm

Research of MPPT Control Method Based on PSO Algorithm

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4th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wang Yunliang Bian Nan Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin Peoples R China Tianjin Univ Technol Sch Control Engn Tianjin Peoples R China

ISBN: (纸本)9781467381734

Because the output characteristics of photovoltaic (PV) array is nonlinear and output power changes as circumstances change, it is necessary to perform Maximum Power Point Tracking (MPPT) control in order to make the PV array working at Maximum Power Point (MPP). When the photovoltaic cells are in partial shade condition, the traditional MPPT method will fail. So this article uses the improved pso algorithm to implement MPPT of the shaded photovoltaic, carry on the modeling and simulation with matlab/simulink. The simulation results show that using pso can track the location of the maximum power point quickly and accurately and get a good control effect.

关键词： photovoltaic cells partial shade maximum power point tracking pso algorithm

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A class of pso-tuned controllers in Lorenz chaotic system

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 430-449页

作者： Dali, Ali Abdelmalek, Samir Bakdi, Azzeddine Bettayeb, Maamar CDER Ctr Dev Energies Renouvelables BP 62 Route Observ Bouzareah 16340 Alger Algeria Univ Medea Fac Technol Dept Elect Engn Medea Algeria Univ Oslo Dept Math N-0851 Oslo Norway Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia

This paper considers the optimal robust control of chaotic systems subject to parameter perturbations and measurement noise. Three novel dynamic tracking controllers are designed herein to suppress the chaotic behaviour in Lorenz systems under practical constraints. The dynamic controllers allow for extra improvements towards optimal and robust tracking but their design is challenging in the chaotic system. Given a single measured state only, the single-state feedback controllers are optimally tuned using high-performance heuristic Particle Swarm Optimization (pso) algorithm with a constrained multiobjective function focusing mainly on the Integral Absolute Error (IAE). The controlled system stability is ensured through Routh-Hurwitz stability criteria where control parameters are further tuned using a metaheuristic pso algorithm to ensure faster transients with minimum errors when tracking desired state trajectories and also to ensure robustness to parameter variations and external disturbances in the chaotic system. Different designs are tested through extensive simulations and comparisons where the proposed pso-tuned PD-tracking controller is proved superior for not only suppressing the chaotic behaviour and reducing the transients but also for their high robustness to parameter uncertainties and external disturbances. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Chaos Lorenz systems Tracking control pso algorithm Robustness

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Research on Controller of Glass Greenhouse Cleaner Based on pso-Fuzzy-PID

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PROCESSES 2023年第12期11卷 3405页

作者： Xu, Rui Chen, Zhenmu Wu, Mingge Shen, Yunde Jiangsu Hengshen Co Ltd Zhenjiang 212314 Peoples R China Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Inst Laser & Optoelect Intelligent Mfg Wenzhou 325035 Peoples R China

When cleaning the surface of a glass greenhouse, the washing machine often encounters slippery tracks or strong winds, in which case the washing machine is very likely to deviate from the axis of the greenhouse. In order to improve the stability and reliability of the glass greenhouse cleaner under different working conditions, this paper studies the controller of the glass greenhouse cleaner and puts forward a fuzzy PID control scheme optimized by the pso algorithm. Using Adams and Matlab to jointly simulate the machine model, the results show that the particle swarm fuzzy PID controller has the best control effect and can make the cleaner adaptable to a variety of working conditions. The system overshoot was reduced by up to 35.5%, the response time by up to 3 s, and the difference between left and right wheel speeds by up to 0.086 deg/s.

关键词： glass greenhouse fuzzy PID co-simulation pso algorithm mathematical model

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Multiscale ultra-short-term wind power prediction model based on GD-IFEM-pso and VMD-BP

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ENERGY SCIENCE & ENGINEERING 2023年第12期11卷 4700-4721页

作者： Zhang, Zhe Wang, Yongsheng Xu, Zhiwei Gao, Jing Liu, Limin Inner Mongolia Univ Technol Sch Data Sci & Applicat Hohhot Peoples R China Software Serv Engn Technol Res Ctr Hohhot Peoples R China Inner Mongolia Agr Univ Sch Comp & Informat Engn Hohhot Peoples R China Inner Mongolia Univ Technol Sch Data Sci & Applicat Hohhot 010080 Peoples R China

Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high-precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra-short-term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD-IFEM-pso) and variational mode decomposition and back propagation network (VMD-BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and alpha values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low-frequency vibration components, high-frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, th

关键词： BP neural network parameter optimization pso algorithm VMD wind power prediction

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A Data Reconciliation-Based Method for Performance Estimation of Entrained-Flow Pulverized Coal Gasification

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ENERGIES 2025年第5期18卷 1079-1079页

作者： Zhang, Yan Yue, Kai Yuan, Chang Xiang, Jiahao Univ Sci & Technol Beijing Sch Energy & Environm Engn Beijing 100083 Peoples R China Beijing MWAY Technol Co Ltd Beijing 100176 Peoples R China Beijing Key Lab Energy Saving & Emiss Reduct Met I Beijing 100083 Peoples R China

Accurate performance estimation of the entrained-flow pulverized coal gasification unit is essential for production scheduling and process optimization, but these are often hindered by inaccurate or insufficient measurements in the industrial system. This paper proposes a data reconciliation-based method to address this challenge. The thermodynamic equilibrium model is employed as constraints of the gasification and quench processes, and the Particle Swarm Optimization (pso) algorithm is applied for parameter estimation. Measured data under stable and variable operating conditions are reconciled, detecting and eliminating a 12% error in syngas flow rate at the scrubber outlet, thereby improving gasification performance accuracy. Two characteristic models concerning carbon conversion rate and the flow rate of reacted quench water are derived from the reconciled results. By combining these models with thermodynamic equilibrium models, the modified R2 of offline predicted syngas flow rate exceeds 0.92, and those of syngas compositions reach 0.72-0.85. Additionally, an Artificial Neural Network (ANN) model, trained on reconciled and predicted data, is proposed for real-time performance estimation. The ANN model calculates performance metrics within 10 s and achieves R2 values above 0.95 for most parameters. This method can be integrated into control systems and serves as a valuable tool for gasification process monitoring and optimization.

关键词： entrained-flow coal gasification data reconciliation pso algorithm ANN real-time performance carbon conversion rate reacted quench water

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Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using pso with Levy flight

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JOURNAL OF ENERGY STORAGE 2022年 49卷

作者： Mao, Xinjian Song, Shaojing Ding, Feng Shanghai Polytech Univ Dept Commun & Informat Engn Shanghai 201201 Peoples R China UAES Dept Syst Shanghai 201201 Peoples R China UAES Elect Control Unit Shanghai 201201 Peoples R China

The battery state of charge (SoC) of lithium batteries for electric vehicles is highly non-linear and time-varying. The convergence speed is slow and the accuracy is low when using ordinary neural network models for SoC estimation. The particle swarm optimization (pso) algorithm based on Levy's flight strategy (Lpso) is proposed to optimize the weights and thresholds of BP neural network, which would improve the prediction accuracy of SoC. According to the mechanism of lithium battery charging and discharging, voltage, current and temperature are selected as input vectors and SoC is selected as output vector. The comparison of the model before and after optimization is carried out by using NASA lithium battery charging and discharging data, which shows this method has better generalization ability and high prediction accuracy. It has practical application significance for SoC estimation.

关键词： SoC estimation Levy flight BP neural network pso algorithm

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A High-Efficiency Codesign Method for Bandgap Circuit by Submodule Optimization

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2025年第0期

作者： Yang, Yunqi Chen, Dongdong Wang, Xianglong Li, Di Yang, Yintang Xidian Univ Fac Integrated Circuit Xian Peoples R China

In this paper, a high-efficiency codesign method for bandgap circuit (BGR) circuit by submodule optimization is proposed. In the proposed method, the BGR circuit is divided into operation amplifier module (OPM) and reference voltage generation module (RVGM) to be separately optimized to improve the optimization design efficiency. Firstly, the OPM is optimized based on neural network (NN) model and particle swarm optimization (pso) algorithm. NN models are established to describe the relationship between design parameters and performance metrics of OPM, and then the parameters of OPM are optimized by pso algorithm according to the established optimization function with constraints. The parameters of RVGM are optimized based on the optimized OPM. The high-accuracy approximate equations of temperature coefficient (TC) are established based on compensation functions. The TC, total area, and total power consumption are co-optimized by interior point method. According to the optimized parameters of OPM and RVGM, the verifications are conducted by Cadence. The results show that the proposed method can greatly improve the design efficiency of BGR circuit, whose total running time is only 552.5 s. Meanwhile, the optimized TC is 4.76 ppm/degrees C over -40 degrees C-125 degrees C at 2.5-V supply, and the optimized area and power consumption are 0.0187 mm2 and 288.5 mu W. The Monte Carlo results show that Vref has a standard deviation of 1.503 mV, which is only 0.12% across 1000 runs, so the Vref is very stable. Therefore, the proposed codesign method can be used to effectively optimize the BGR circuit.

关键词： bandgap reference circuit codesign optimization method NN model pso algorithm

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Experimental Parameter Identification of Lead-Acid Batteries using pso Optimization 2

Experimental Parameter Identification of Lead-Acid Batteries...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Bouharchouche, Abderrezzaq Dali, Ali Kaabeche, Abdelhamid Berkouk, El Madjid Ctr Dev Energies Renouvelables CDER Algiers 16340 Algeria Ecole Natl Polytech Lab Commande Proc LCP Algiers Algeria

ISBN: (纸本)9798350349740;9798350349757

This study proposes a technique for parameter identification of lead-acid batteries using the Particle Swarm Optimization (pso) algorithm. The methodology uses a detailed mathematical model of the batteries to accurately estimate the parameters representing their actual behavior within a predefined margin of error. Experimental validation demonstrates the effectiveness of the proposed approach in achieving precise parameter identification.

关键词： Lead-Acid Batteries Parameter Identification Battery Modelling pso algorithm

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