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Stable Control Strategy for Planar Three-Link Underactuated Mechanical System

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2016年第3期21卷 1345-1356页

作者： Lai, Xuzhi Wang, Yawu Wu, Min Cao, Weihua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

A stable-control strategy for a planar threelink passive-active-active underactuated mechanical system not subject to gravity constraints has been devised. The control objective is to move the end effector from any initial position to any target position. First, a dynamic model of the system is built, and its properties are analyzed. Next, based on the complete integrability of an underactuated planar acrobot (UPA), the control of a planar three-link system is divided into two stages. In each stage, keeping the angle of one active link constant reduces the planar three-link system to a UPA, thereby enabling the use of quadrature to obtain the angle constraint relationships between the passive and active links. Then, the target angles associated with the target position are calculated by particle swarm optimization based on the angle constraint relationships. Finally, a controller for each stage is designed, ensuring that the control objective will be reached. Simulation results demonstrate the validity of this control method.

关键词： Lyapunov function planar underactuated mechanical system pso algorithm state constraint relationship

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Joint inversion of TEM and DC in roadway advanced detection based on particle swarm optimization

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JOURNAL OF APPLIED GEOPHYSICS 2015年 123卷 30-35页

作者： Cheng, Jiulong Li, Fei Peng, Suping Sun, Xiaoyun Zheng, Jing Jia, Jizhe China Univ Min & Technol State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China North China Inst Sci & Technol Key Lab Mine Disaster Prevent & Control Beijing 101601 Peoples R China Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Peoples R China

Transient electromagnetic method (TEM)and direct current method (DC)are two key widely applied methods for practical roadway detection, but both have their limitations. To take the advantage of each method, a synchronous nonlinear joint inversion method is proposed based on TEM and DC by using particle swarm optimization (pso)algorithm. Firstly, a model with double low resistance anomaly and interference is constructed to test the performance of the method. Then the independent inversion and joint inversion are calculated by using the model built above. It is demonstrated that the joint inversion helped in improving the interpretation of the data to get better results. It is because that the suppression of interference and separation of the resistivity anomalies ahead and the back of the roadway working face using the proposed method. Finally, the proposed method was successfully used in a coalmine in Huainan coalfield in east China to demonstrate its practical usefulness. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Whole-space Advanced detection Transient electromagnetic method Direct current method Joint inversion pso algorithm

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Surface roughness modeling and milling parameter optimization of 15-5PH stainless steel in milling arc surface

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第4期40卷 1-9页

作者： Zheng, Kan Yao, Jingdong Dong, Song Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China Cent South Univ Changsha 410006 Hunan Peoples R China

In this study, a comprehensive prediction model of surface roughness on arc surface of 15-5PH stainless steel is constructed, which is considered to consist of the certain part and uncertain part. The certain part considered to be dependent on path interval and scallop height is formulated in theory using kinematics analysis and geometric modeling. The uncertain part related to the machining system stability, cooling condition and milling modes is empirically predicted by partial least squares based on experimental data. Then, the prediction model is integrated into particle swarm optimization algorithm to find the optimal parameters for the maximum material removal rate (MRR). Finally, the verification experiments considering validation of prediction model and optimal parameters are conducted. The experimental results show that the prediction accuracy presents well with the mean prediction error 10.49%. The optimization also satisfies the constraint of surface roughness with a 262% increase in MRR.

关键词： Milling Surface roughness Partial least squares pso algorithm Parameters optimization

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Corrective action planning considering FACTS allocation and optimal load shedding using bacterial foraging oriented by particle swarm optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2010年第4期18卷 597-612页

作者： Hooshmand, Rahmat Allah Pour, Mostafa Ezatabadi Univ Isfahan Dept Elec Eng Esfahan *** Iran

Reactive power planning (RPP) involves optimal allocation and determination of the type and size of new reactive power (VAR) supplies to satisfy voltage constraints during normal and contingency states. The RPP issue is in fact an optimization of large scale mixed integer nonlinear programming problem, so it is proper to use an evolutionary algorithm to solve the problem. In this paper, in order to solve the RPP problem for corrective action of power systems, the bacterial foraging (BF) oriented by particle swarm optimization (pso) algorithm (BF-pso) is proposed. In the algorithm, the VAR control has been carried out by using flexible AC transmission systems (FACTS) devices, in order to minimize the installation costs of these devices. In order to determine the saving rate in the costs, corrective control is also performed by the utilization of load shedding algorithm. The IEEE 57-Bus system, is used to test the proposed method. The simulation results of the proposed algorithm are compared with pso and genetic algorithms (GA) to show the efficiency of this method in the RPP problem.

关键词： FACTS Devices Reactive Power Planning Load Shedding BF algorithm pso algorithm

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Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2020年第1期34卷 114-126页

作者： Harandizadeh, Hooman Shahid Bahonar Univ Kerman Dept Civil Engn Fac Engn Pajoohesh SqImam Khomeni HighwayPOB 76169133 Kerman *** Iran

This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (pso) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-pso. The derived database provides information related to pile load tests, in situ field CPT data, and soil-pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-pso model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-pso network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-pso model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

关键词： ANFIS algorithm CPT-based models GMDH type neural network pile bearing capacity pso algorithm

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NSNpso-INC: A Simplified Particle Swarm Optimization algorithm for Photovoltaic MPPT Combining Natural Selection and Conductivity Incremental Approach

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IEEE ACCESS 2024年 12卷 137760-137774页

作者： Xu, Shi-Zhou Zhong, Yi-Ming Henan Normal Univ Coll Elect & Elect Engn Xinxiang 453002 Peoples R China

To enhance photovoltaic (PV) system performance under various environmental conditions, this paper proposes a hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates the Incremental Conductance (INC) method with an improved Particle Swarm Optimization (pso) algorithm. The proposed approach simplifies the pso velocity update process and uses natural selection to filter less adaptive particles. Leveraging the rapid convergence of the INC method, this hybrid algorithm effectively tracks the maximum power point (MPP). Simulation results show that under static partial shading conditions, the NSNpso-INC algorithm achieves a stable MPPT efficiency of 99.9%, with a 68.18% faster convergence than conventional pso and more stable performance than traditional INC. Under dynamic irradiance conditions, NSNpso-INC adapts quickly, improving convergence speed by 93.33% over conventional pso. Experimental results further demonstrate that NSNpso-INC reaches steady state fastest, with an 85.52% improvement in convergence speed over traditional pso. The algorithm maintains a stable power output around 550W even under cloudy conditions, significantly enhancing PV energy conversion efficiency and providing valuable insights for future MPPT algorithm development.

关键词： PV array PV array MPPT MPPT pso algorithm pso algorithm boost converter boost converter

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Spider-based FOPID controller design for temperature control in aluminium extrusion process

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2022年第1期41卷 493-504页

作者： Murugesan, Ramasubramanian Marimuthu, Thirumarimurugan Coimbatore Inst Technol Dept Chem Engn Coimbatore 641014 Tamil Nadu India

Based on the exit temperature in the aluminium extrusion process, the quality of the aluminium bars is obtained in the industries. Control of temperature plays a significant role to obtain the quality of the aluminium bars or rods. Problems are still encountered in the design of proper temperature control schemes in the industries. A new control scheme is presented here, to address the temperature control in the aluminium extrusion process. Spider-based fractional-order PID controller scheme is simulated for the aluminium extrusion plant using MATLAB and its toolbox. It gives the improved result for reducing the time delay, rise time and settling time in the process. Integral square error performance criteria are considered an objective function. The minimization of objective function gives good results. Comparisons of the presented control scheme with the conventional control methods are presented.

关键词： aluminium extrusion process temperature control FOPID controller pso algorithm spider algorithm

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Novel Approach for Reducing Train Wheel and Brake Shoe Wear Using Speed Profile Optimization

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TRANSPORTATION RESEARCH RECORD 2024年第9期2678卷 241-250页

作者： Adeli, Hasan Mirabadi, Ahmad Yousefi, Shahin Fazel, Saeed Iran Univ Sci & Technol Sch Railway Engn Tehran Iran

In this article, we propose a new approach for generating a train's speed profile that takes into account the wear caused by the wheel-rail interaction and brake shoe wear. This method involves modeling the train and its path and simulating the train's movement to calculate the wear caused by the wheel-rail interaction and brake shoe wear resulting from mechanical braking. Finally, using the particle swarm optimization algorithm, an optimal speed profile is produced, which minimizes the total wear. The results of implementing this method on the Shahriar-Roodshour route demonstrate that using an optimal speed profile results in a significant wear reduction of over 40%.

关键词： speed profile optimization wear reduction railway pso algorithm wheel and brake wear

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A multiple heterogeneous kernel RVM approach for analog circuit fault prognostic

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S3849-S3861页

作者： Zhang, Chaolong He, Yigang Yuan, Lifen Xiang, Sheng Anqing Normal Univ Sch Phys & Elect Engn Anqing 246011 Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China

This paper presents a multiple heterogeneous kernel relevance vector machine (MHKRVM) approach for analog circuit fault prognostic. Compared to other kernel learning methods, multiple kernel learning method produces the optimal kernel function because many effective kernels' combination always generates better generalization performance. By using the multiple heterogeneous kernel learning method, the proposed MHKRVM method's kernel function holds its diversification. Meanwhile, the sparse weights of consisted basic kernels in the MHKRVM are helpful in prediction accuracy, and they are yielded through particle swarm optimization algorithm. Six fault prognostic cases are conducted to demonstrate the whole prognostic procedure, and prove that the presented MHKRVM can predict the trend of falling circuit elements' health degree trajectories closely and estimate the remaining useful performance of failing circuit elements accurately.

关键词： Analog circuits Fault prognostic Relevance vector machine Multiple heterogeneous kernel learning method pso algorithm

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Pilot tones design using particle swarm optimization for OFDM-IDMA system

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NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 5299-5308页

作者： Taspinar, Necmi Simsir, Sakir Erciyes Univ Fac Engn Dept Elect & Elect Engn TR-38039 Kayseri Turkey

Since the channel estimation performance is directly affected by the pilot positions, it has been a major task in multicarrier transmission schemes to adjust the pattern of pilot distribution for the purpose of minimizing the estimation errors. Therefore, in this paper, we utilize particle swarm optimization (pso) algorithm for pilot design process in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) system. The main contributions of the paper are: (1) increasing the performance of least squares (LS) algorithm used for channel estimation in OFDM-IDMA system by optimizing the pilot positions using pso algorithm, (2) instead of using MSE itself in which the matrix inversion process is required as the fitness function of pso, using the upper bound of mean square error (MSE) in order to decrease the system complexity, (3) using two types of channel models known as COST 207 Rural Area and COST 207 Typical Urban in the simulations to be able to support the reliability and stability of the proposed method in different conditions. In the simulations, it is observed from the bit error rate (BER) and MSE graphs that optimizing the placement of pilot tones using our proposed method demonstrates a superior performance compared to the other considered pilot placement strategies by providing a significant increase in the performance of LS algorithm.

关键词： OFDM-IDMA Pilot tones design Channel estimation GA pso algorithm

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