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Sliding mode control based on particle swarm optimization neural network and adaptive reaching law

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年第4期46卷 741-751页

作者： Chen, Jiqing Zhang, Haiyan Pan, Shangtao Tang, Qingsong Guangxi Univ Sch Mech Engn Nanning 530005 Peoples R China

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.

关键词： Manipulators sliding mode control RBFNN particle swarm optimization algorithm

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Multivariable PID Decoupling Control Method of Electroslag Remelting Process Based on Improved particle swarm optimization (PSO) algorithm

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INFORMATION 2014年第1期5卷 120-133页

作者： Wang, Jie-Sheng Ning, Chen-Xu Yang, Yang Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Liaoning Peoples R China

A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller tuned optimally by an improved particle swarm optimization (IPSO) algorithm is proposed to control the two-input/two-output (TITO) ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.

关键词： electroslag remelting process multivariable system PID controller particle swarm optimization algorithm

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particle swarm optimization using Adaptive Inertia Weight for Optimal Capacitor Bank Placement in Radial Distribution Networks

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第11期38卷 2675-2684页

作者： Castillo, C. A. Lopez Tecsi, A. V. Huillcahuaman Gastelu, J. Villavicencio Obispo, A. E. Univ Tecnol Peru Lima Peru

The steady increase in energy demand results in under-voltage problems and an increase in the active power losses in electrical distribution networks. The optimal placement of capacitor banks in distribution systems is essential for enhancing the voltage profile and reducing power losses. This paper proposes a modified particle swarm optimization (PSO) algorithm using an adaptive inertia constant to expand the search space and determine the optimal locations and sizes of capacitor banks. Two loss sensitivity indices (LSI) were applied to identify candidate buses. This methodology was applied to the IEEE 33-bus radial distribution system considering two scenarios: one with fixed capacitors (Case 1) and the other with switched capacitors (Case 2). The results demonstrate that the proposed algorithm effectively reduces system losses by 31.30% and 31.38% in Cases 1 and 2, respectively. In addition, in both cases, the voltage profiles were improved and maintained within allowable limits. Therefore, the proposed methodology is expected to work well in larger electrical distribution networks.

关键词： Electrical Distribution Networks Capacitor Banks particle swarm optimization algorithm Under Voltage Power Losses

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Data dissemination protocol for VANETs to optimize the routing path using hybrid particle swarm optimization with sequential variable neighbourhood search

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TELECOMMUNICATION SYSTEMS 2023年第2期84卷 153-165页

作者： Gopalan, S. Harihara Ashok, J. Manikandan, A. Ramalingam, S. Sri Ramakrishna Engn Coll Coimbatore India VSB Engn Coll Dept Elect & Commun Kaur India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindugal 624002 India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 642109 India

A vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (MANET) which employs wireless routers that are inside every vehicle to operate as a node. The process of data dissemination is used to improve the quality of travel to avoid unnecessary accidents in VANET. Many legacy protocols use this type of messaging activity to ensure fair road safety without concern for network congestion. Node congestion increases with control of routing overhead packets. Therefore, this paper proposes a Data Dissemination Protocol (DDP). VANET routing protocols can be divided into two categories: topology-based routing protocols and location-based routing protocols. The goal is to relay emergency signals to stationary nodes as soon as possible. The standard messages will be routed to the FIFO queue. Multiple routes were found using the Time delay-based Multipath Routing (TMR) approach to transmit these messages to a destination node, and particle swarm Optimisation (PSO) is utilized to find the optimal and secure path. Sequential Variable Neighborhood Search (SVNS) algorithm is applied in order to optimize the particles' position with Local Best particle and Global Best particle (LBGB). The proposed method PSO-SVNS-LBGB is compared with different methods such as PSO-SVNS-GB, PSO-SVNS-LB, PSO-SVNS-CLB, PSO-SVNS-CGB. The experimental results show significant improvements in throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and energy consumption. The simulation environment was conducted in NS2.34 is preferred for network simulation, and the VANET simulator used is SUMO and MOVE software. With a 98.41 ms delay and an average speed of 60 km/h, the PSO-SVNS-LBGB approach is suggested.

关键词： Vehicular ad-hoc networks (VANETs) Bio-inspired routing Meta-heuristics particle swarm optimization algorithm Sequential variable neighbourhood search

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Studying the geometrical models of tubular braided composite using micro computed tomography and particle swarm optimization

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COMPOSITES PART B-ENGINEERING 2023年 260卷

作者： Gholami, Ali Melenka, Garrett W. York Univ Dept Mech Engn Toronto ON Canada

This paper compares the geometrical model of a 2DTBC with its actual model generated by the Micro-Computed Tomography (& mu;CT) method. First, the geometrical models' equations are edited to simulate the manufacturing process more accurately, and the equations are incorporated into the TBC-Gen program. Then, a & mu;CT scan is done on a cured carbon fiber TBC, and 2D and 3D models are created. Next, two yarns of the scanned models are extracted and modelled. The segmented yarns are analyzed, and their geometrical data, including cross-section area, major and minor yarn diameters, orientation, mandrel diameter, portion angle and centre points, are extracted. Next, a yarn path simulating the scanned yarn is generated using the TBC-Gen and the extracted parameters. Then, the generated geometrical model is compared with the & mu;CT model. To do that, a parameter named portion angle is introduced to help the geometrical model better fit the & mu;CT model. Finally, particle swarm optimization (PSO) is used to optimize the portion angle. The result of the fitting algorithm shows the accuracy of the geometrical model (less than 1% error) to simulate actual TBC. Understanding the amount of error between a geometrical model and the actual model will help to evaluate the application of geometrical models more thoroughly. Also, the more accurate geometrical model will contribute less error to the FEM simulation. The quantitative comparison between these two models can give a clear understanding of the amount of error existing in the geometrical model compared to an accurate model generated by & mu;CT.

关键词： Braided composite material Mico-CT Geometrical model Inverse problem particle swarm optimization algorithm

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Regional Load Frequency Control of BP-PI Wind Power Generation Based on particle swarm optimization

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ENERGIES 2023年第4期16卷 2015-2015页

作者： Sun, Jikai Chen, Mingrui Kong, Linghe Hu, Zhijian Veerasamy, Veerapandiyan Qingdao Univ Coll Elect Engn Qingdao 266071 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10(-3) for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.

关键词： wind power generation sudden load disturbance load frequency control BP neural network particle swarm optimization algorithm

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Hardware implementation of particle swarm optimization with chaotic fractional-order

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 11249-11268页

作者： Zermani, Aymen Manita, Ghaith Feki, Elyes Mami, Abdelkader Tunis El Manar Univ LAPER Dept Phys Fac Sci Campus Univ El Manar Tunis 2092 Tunisia Univ Sousse Lab MARS ISITCom LR17ES05 Sousse Tunisia

Engineering optimization methods based on meta-heuristic algorithms are computationally demanding and might present complex implementation issues for embedded devices with high constraints. One of the interesting meta-heuristic methods frequently utilized to address diverse optimization issues in the real world is particle swarm optimization (PSO). However, the most challenging constraints in real applications;having an optimal execution time for an optimal solution, added to avoid premature convergence and stagnation in the local optima. In this study, we propose the hardware implementation of an improved particle swarm optimization algorithm by using the fractional order concept and chaos theory named CF-PSO. The concept is to substitute a chaotic sequence generator for the values of random variables and to use fractional calculus in the velocity and position expressions. We provide a hardware design of the enhanced PSO on Field Programmable Gate Array (FPGA) to obtain substantially quicker execution speeds than those attainable in software implementation. A Xilinx Virtex-7 Pro Development Kit is used to implement the hardware-improved PSO design and evaluate its performance. To start, we put the suggested architecture to the test using a benchmark of four functions to demonstrate the effectiveness of this hardware implementation. Secondly, we contrasted it with three other PSO variations: the conventional PSO, the PSO using fractional order velocity (F-V-PSO), and the PSO using fractional order velocity and position (F-VP-PSO). The experimental results show that the hardware implementation of F-VP-PSO is between 1.082 and 21.25 times faster. CFPSO algorithm can be implemented in the Virtex7 VC707 evaluation platform by utilizing less than 2% of the slice 462 registers and 16% of the slice LUT (Look up Table) resources.

关键词： particle swarm optimization algorithm Fractional-order difference Chaos theory FPGA Hardware

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Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第14期237卷 3334-3352页

作者： Ghorbany, Mohammad Ebrahimi-Nejad, Salman Mollajafari, Morteza Iran Univ Sci & Technol Sch Automot Engn Vehicle Dynam Syst Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Farjam StHengam StResalat Sq Tehran *** Iran

In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective particle swarm optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.

关键词： Multi-objective optimization pneumatic springs particle swarm optimization algorithm optimal vehicle dynamics performance ride quality handling

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Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期212卷

作者： Zhao, Xin Zhang, Lei Zhu, Ge Cheng, Chenguang He, Jun Traore, Seydou Singh, Vijay P. North China Univ Water Resources & Elect Power Sch Water Conservat Zhengzhou 450045 Henan Peoples R China North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou 450045 Henan Peoples R China Henan Water Conservancy Survey Design & Res Co Ltd Zhengzhou 450046 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Hubei Peoples R China Texas A&M Univ Dept Biol & Agr Engn Zachry Dept Civil & Environm Engn College Stn TX 77843 USA Metropolitan Solar Inc Washington DC 20032 USA UAE Univ Natl Water & Energy Ctr Al Ain 15551 U Arab Emirates

Accurate estimation of crop evapotranspiration (ETc) is crucial for improving the water use efficiency and designing and operating irrigation systems. To accurately calculate winter wheat ETc with limited meteorological data, the present study proposed two interpretable machine learning (ML) models (random forest (RF) and extreme gradient boosting (XGBoost)) as well as non-interpretable ML models (support vector machine (SVM) and deep neural network (DNN)) based on the particle swarm optimization (PSO) algorithm using observed winter wheat ETc data during the period from 2007 to 2013 at Luan Cheng Agro-ecosystem Experimental Station. Mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R-2), and global performance indicator (GPI) were used to assess the performance of models. This demonstrated that the ML models based on the crop coefficient (Kc) and solar radiation (Rn) were accurate and offered a workaround for calculating winter wheat ETc in the absence of meteorological data. In four ML models, the ninth input combination, consisting of Kc, Rn, daily air maximum temperature (Tmax), daily air minimum temperature (Tmin), sunshine hours (n), and wind speed with a height of 2 m (U2), produced the best estimate of ETc. Among them, the PSO-based SVM (PSO-SVM) model obtained the best results for estimating ETc with MAE, RMSE, NSE, R2, and GPI values of 0.389 mm center dot d(-1), 0.562 mm center dot d(-1) 0.910, 0.911, and 0.975, respectively, showing the advantages of the non-interpretable ML model in ETc forecasting. Accurate descriptions of actual hydrological and climatic processes were given by local interpretable model-agnostic explanations (LIME). The inflection points of daily climatic parameters (Tmin, Tmax, Rn, n) related to ETc were determined to be 3.80 degrees C, 5.50 degrees C, 1.62 MJ center dot m(-2)center dot d(-1), 1.37 h, respectively. This work has potential to overcome the difficult

关键词： Winter wheat Crop evapotranspiration particle swarm optimization algorithm Machine learning Interpretability

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Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

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ENERGY 2013年 55卷 497-510页

作者： Rashidi, M. M. Ali, M. Freidoonimehr, N. Nazari, F. Bu Ali Sina Univ Fac Engn Dept Mech Engn Hamadan Iran King Saud Univ Coll Engn Dept Mech Engn Riyadh 11421 Saudi Arabia Islamic Azad Univ Hamedan Branch Young Researchers & Elites Club Hamadan Iran

The present study first of all concerns the first and second law analyzes of an electrically conducting fluid past a rotating disk in the presence of a uniform vertical magnetic field, analytically via Homotopy Analysis Method (HAM), and then applies Artificial Neural Network (ANN) and particle swarm optimization (PSO) algorithm in order to minimize the entropy generation. In the first part of this study, entropy generation equation is derived as a function of velocity and temperature gradients and non-dimensionalized using geometrical and physical flow field-dependent parameters. A very good agreement can be seen between some of the obtained results of the current study and the results of the previously published data. The effects of physical flow parameters such as magnetic interaction parameter, unsteadiness parameter, disk stretching parameter, Prandtl number, Reynolds number and Brinkman number on all fluid velocity components, temperature distribution and the averaged entropy generation number are checked and analyzed. For minimizing the entropy generation value a procedure based on ANN and PSO is proposed. This procedure comprises three steps. The first step is to find entropy generation for values of some different affecting factors. In the second step, some distinct multi-layer perceptron ANNs based on the data obtained from step one are trained. In step three, PSO is used to minimize the entropy generation in the considered stretchable rotating disk. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： MHD flow Entropy generation Unsteady flow Rotating disk particle swarm optimization algorithm Artificial neural network

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