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A new effective robust nonlinear controller based on pso for interleaved DC-DC boost converters for fuel cell voltage regulation

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SOFT COMPUTING 2020年第22期24卷 17051-17064页

作者： Abdelmalek, Samir Dali, Ali Bettayeb, Maamar Bakdi, Azzeddine Univ Medea Fac Technol Dept Elect Engn Medea Algeria Renewable Energy Dev Ctr CDER Algiers Algeria Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia Univ Oslo Dept Math N-0851 Oslo Norway

Output voltage regulation of DC-DC converters has recently gained an increasing attention to face the many system nonidealities. The fast switching behavior is nonlinear time varying, the presence of model and measurement uncertainties, and large variations, are all inherited challenges. The aim of the present work is to design a robust nonlinear controller that ensures satisfactory and robust output voltage regulation for a proton-exchange membrane fuel cell (PEMFC) based on a DC-DC Interleaved Boost Converter (IBC). A state-space model of the DC-DC IBC is first derived using the state-space averaging technique, and a mathematical model is constructed for the PEFMC. In this regard, a robust nonlinear controller and a proportional integral controller are proposed. The controllers are tuned though particle swarm optimization algorithm to estimate their good parameters assuring the desired performance is met. The integral of absolute error criterion is used to improve the dynamic performance of the overall controlled system. Furthermore, the closed-loop stability is analyzed using the Lyapunov stability theorem, and the effectiveness of the closed-loop system is validated under various operating conditions of the PEMFC and load perturbations. Compared to other methods, the obtained results demonstrate a superior performance of the proposed control strategy in terms of its robustness to variations and uncertainties, smooth tracking of a varying set-point and faster transients.

关键词： Interleaved boost converter Proton-exchange membrane fuel cell pso algorithm Nonlinear control pso-PI controller Current ripples

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Adaptive active queue management controller for TCP communication networks using pso-RBF models

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NEURAL COMPUTING & APPLICATIONS 2013年第5期22卷 933-945页

作者： Sheikhan, Mansour Shahnazi, Reza Hemmati, Ehsan Islamic Azad Univ Dept Commun Engn Fac Engn South Tehran Branch Tehran Iran Islamic Azad Univ Modeling & Optimizat Res Ctr Sci & Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) is a promising technique to congestion control for reducing packet loss and improving network utilization in transmission control protocol (TCP)/Internet protocol (IP) networks. AQM policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts, and the adoption of a suitable control policy. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF as a nonlinear controller is suitable as an AQM scheme to control congestion in TCP communication networks since it has nonlinear behavior. Particle swarm optimization (pso) algorithm is also employed to derive RBF output weights such that the integrated-absolute error is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The output weights and the coefficient of the integral error term in the latter controller are also optimized by pso algorithm. It should be noted that in both proposed controllers the parameters of radial basis functions are selected to symmetrically partition the input space. The results of the comparison with adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

关键词： Active queue management RBF neural network pso algorithm Communication network

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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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Applied Research of ADSHpso algorithm in Multi-UAV Cooperative Mission Planning

Applied Research of ADSHPSO Algorithm in Multi-UAV Cooperati...

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International Conference on Mechatronics, Robotics and Automation (ICMRA 2013)

作者： Wang, Xinzeng Xiao, Jinbao Luoyang Electron Equipment Test Ctr Xinyang Peoples R China

ISBN: (纸本)9783037858066

The particle swarm optimization algorithm was improved in this paper, a novel self-adaption dynamic sub-swarms hybrid particle swarm optimization algorithm is proposed, in this algorithm, subgroup partition method based on dynamic clustering of particle adaptive value is adopted to divide particle group to different capability sub-group, then execute different optimize strategy to different subgroup, simultaneity, inertial weight and accelerating coefficient are adaptive set, through contacting adjustment of parameter with sub-group capability, the particle mode method and intersect and aberrance strategy of double deck are designed, The experimental results show that the algorithm has simple programming, good robust capability and strong optimizing capability which established the foundation of task planning of multi-UAV Cooperative.

关键词： pso algorithm Improve Strategy Unmanned Aerial Vehicle (UAV) Mission Planning

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A New RBF Neural Network Training algorithm Based on pso

A New RBF Neural Network Training Algorithm Based on PSO

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Dingxue Zhang,Xinzhi Liu,Zhihong Guan Control science and technology Department,Huazhong University of Science & Technology Wuhan,Hubei,430074,China

Based on the study of Radial Basis Function(RBF) neural network training algorithm and Particle Swarm Optimization(pso) algorithm,a new RBF neural network training algorithm with modified pso algorithm is formulated,in which a control gene is introduced into basis pso *** algorithm can determine network structure and parameters,such as centers and widths of hidden units by combining with least square *** new training algorithm is applied to the nonlinear system identification problem,comparing with hierachical genetic algorithm and orthogonal least squares algorithm(OLS),the simulation results illustrate its efficiency.

关键词： RBF neural network pso algorithm least square method nonlinear system identification

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A new development of ANFIS-GMDH optimized by pso to predict pile bearing capacity based on experimental datasets

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ENGINEERING WITH COMPUTERS 2021年第1期37卷 685-700页

作者： Harandizadeh, Hooman Jahed Armaghani, Danial Khari, Mahdy Shahid Bahonar Univ Kerman Fac Engn Dept Civil Engn Pajoohesh SqPOB 76169133 Kerman Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Islamic Azad Univ East Tehran Branch Dept Civil Engn Tehran Iran

Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (pso) algorithm called as ANFIS-GMDH-pso model;the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN-GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS-GMDH model achieved relatively higher performance compared to ANN and FPNN-GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

关键词： Ultimate pile bearing capacity Deep foundation ANFIS– GMDH– pso model pso algorithm FPNN– GMDH model GMDH network

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Import and export trade forecasting algorithm based on blockchain security and pso optimized hybrid RVM model

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SECURITY AND PRIVACY 2023年第2期6卷

作者： Wang, Yue Guizhou Inst Technol Coll Econ & Management Guiyang 550003 Guizhou Peoples R China

With the rapid development of globalization economy, countries in the world are more and more closely connected in business, and international import and export trade has also achieved rapid development. Based on the analysis of the status quo of the country's import and export trade, this article combines the main national economic indicators to analyze and study the algorithm of import and export trade forecasting. RVM is a sparse probability model and a new supervised learning method. Firstly, this paper introduces the protection system of block chain technology for import and export trade data information. Then, a hybrid RVM model is constructed, which is optimized by pso algorithm, and the import and export volume is predicted based on this model. Based on the analysis of the data, it is tested that the security protection coefficient of the blockchain security technology for import and export trade data can reach up to 99.9%, and the accuracy of the import and export trade forecast based on the pso optimized hybrid RVM model can reach up to 85.79%. A series of experiments show that the import and export trade forecast model studied in this paper can accurately predict the volume of import and export trade, thus providing a new method for the country's import and export trade, with high practical significance.

关键词： blockchain security hybrid RVM model import and export trade forecast pso algorithm

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The optimal control synchronization of complex dynamical networks with time-varying delay using pso

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NEUROCOMPUTING 2019年 333卷 1-10页

作者： Chang, Qi Yang, Yongqing Sui, Xin Shi, Zhicheng Jiangnan Univ Wuxi Engn Res Ctr Biocomp Sch Sci Wuxi 214122 Peoples R China Jiangnan Univ Sch IoT Engn Wuxi 214122 Peoples R China

The paper mainly deals with the exponential synchronization of complex dynamical networks with a time-varying delay. Based on the event-triggered control, some sufficient conditions for exponential synchronization of complex dynamical networks can be achieved by using the Lyapunov function method, along with the Linear Matrix Inequality (LMI) and matrix analysis. The Zeno-behavior is excluded for the designed event-trigger controller. The optimal control parameters satisfying a minimal integral square error (ISE) index and control energy can be computed by using the particle swarm optimization (pso) algorithm. A numerical example is given to demonstrate the effectiveness and applicability of the proposed approach. (C) 2018 Published by Elsevier B.V.

关键词： Exponential synchronization Complex dynamical networks Event-triggered control Optimal control pso algorithm

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Automatic speech patterns recognition of commands using SVM and pso

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第22期78卷 31709-31731页

作者： Batista, Gracieth Cavalcanti Silva, Washington Luis Santos de Oliveira, Duarte Lopes Saotome, Osamu Technol Inst Aeronaut 50 Vila Acacias Sao Jose Dos Campos SP Brazil Fed Inst Maranhao 4 Monte Castelo Sao Luis MA Brazil

This paper proposes the implementation of an Automatic Speech Recognition (ASR) process through extraction of Mel-Frequency Cepstral Coefficients (MFCCs) from voice signal commands, application of the Discrete Cosine Transform (DCT) in these coefficients, Support Vector Machine (SVM) training optimized by the Particle Swarm Optimization (pso) technique in order to speed up the whole process and using One Against All (OAA) multiclass SVM classification. The main contribution is in training phase that it is the combination of SVM with pso algorithm, resulting in computational load and processing time reduction. This novel algorithm is called here as pso-SVM hybrid training application and its performance is shown as the experimental results of voice signal commands in Brazilian Portuguese language. Such commands comprise 10 isolated digits (from zero to nine) and 20 action commands such as "go ahead", "finish", "pause", etc.;that is, there are 30 different pattern types (classes) to be separated (recognized). The process is speaker independent type, that is, the voice bank used in training is different from the one used in tests. The obtained results presented success rates of 92% to 99% during the tests for the classifier using RBF kernel function. Besides, the comparison section shows that this technique is 25 times faster than the recognition without optimization and also, it presents 10% of improvement in recognition success rate when compared to the well-known technique, Gaussian Mixture Models (GMM) algorithm. In addition, the proposed algorithm can be applied in any data processing board for voice signals (DSP, FPGA, DSPIC, ...).

关键词： pso-SVM hybrid training pso algorithm SVM multiclass Speech patterns recognition

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Development of GWO-DSO and pso-DSO hybrid models to redesign the optimal dimensions of labyrinth spillway

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SOFT COMPUTING 2019年第15期23卷 6391-6406页

作者： Tabari, Mahmoud Mohammad Rezapour Hashempour, Masoumeh Shahrekord Univ Dept Engn Shahrekord Iran

In this study, considering the importance of dams spillways redesign to provide optimal dimensions, the hybrid of particle swarm optimization, gray wolf optimization (GWO) and direct search optimization meta-heuristic approach were proposed. In this methodology, the total volume of spillway body, which indicates the amount of concrete and cost, is considered as the objective function and height of spillway, the number of cycles, apex length and angle of wall are defined as decision variables. By implementing the proposed model based on the data of the Ute dam labyrinth spillway, the optimal dimensions of spillway were determined and compared with the other studies. The results indicated that the hybrid of meta-heuristic algorithms has a very good performance in generating global optimal values, except that the GWO algorithm has a higher convergence rate. In the proposed method, the optimal dimensions provided by the hybrid algorithm led to saving in concrete and reduction of costs to 64% as compared to the existing design of dam. Investigating the optimal dimension of trapezoidal labyrinth spillway also indicates that these dimensions increase the flow capacity of the spillway to 15,760 m3/s. Finally, comparison of the optimum dimensions obtained with two trapezoidal and triangular sections showed that the optimal form for labyrinth spillway is triangular and leads to improved spillway hydraulic performance.

关键词： Labyrinth spillway Optimization of labyrinth dimensions pso algorithm Gray wolves algorithm Ute dam

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