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Fractional-order control for nano-positioning of piezoelectric actuators

INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2022年第22期36卷 2250134-2250134页

作者： Yang, Liu Zhang, Yi Zhao, Zhongyang Li, Dongjie Harbin Univ Sci & Technol Sch Automat Harbin 150000 Peoples R China Key Lab Complex Intelligent Syst & Integrat Heil Harbin 150000 Peoples R China

Piezoelectric actuators are widely used in the field of high-precision positioning control. However, due to the inherent hysteresis nonlinearity of piezoelectric actuators, it can seriously affect their accuracy in tracking and positioning systems, and even cause system instability. In order to suppress the hysteresis characteristics of piezoelectric, based on the analysis of piezoelectric characteristics and the characteristics of classical Bouc- Wen model, a Bouc-Wen model is adopted to describe the hysteresis characteristics of piezoelectric actuators, an artificial bee colony (ABC) algorithm is used to identify Bouc-Wen. The maximum error of the model is 2.49%. According to the established hysteresis model, its inverse model is obtained as the feedforward controller, and the inverse model compensation method is used to realize the open-loop control of the system. Since the feedforward controller based on the inverse model cannot eliminate the modeling errors and external disturbances of the inverse model, a fractional-order controller is designed on the basis of inverse compensation, and a compound control method combining feedforward control and feedback control is adopted. Experiments show that the fractional-order control system has fast response speed, small steady-state error and simple structure compared with other nonlinear controllers, positioning error is about 3%.

关键词： Piezoelectric actuator hysteresis nonlinearity artificial bee colony algorithm fractional order (PID mu)-D-lambda micro-components

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A DISCRETE BUTTERFLY-INSPIRED OPTIMIZATION algorithm FOR SOLVING PERMUTATION FLOW-SHOP SCHEDULING PROBLEMS

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NEURAL NETWORK WORLD 2020年第4期30卷 211-229页

作者： Qi, X. Yuan, Z. Han, X. Liu, S. Shenyang Univ Sch Mech Engn Shenyang 110044 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

Permutation Flow-Shop Scheduling Problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm - artificial Butterfly Optimization (ABO) algorithm, this paper presents a Discrete artificial Butterfly Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total flow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem.

关键词： artificial Butterfly Optimization artificial bee colony algorithm particle swarm optimization Permutation Flow-Shop Scheduling Problem

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Performance-aware deployment of streaming applications in distributed stream computing systems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第1期15卷 52-62页

作者： Sun, Dawei Gao, Shang Liu, Xunyun Li, Fengyun Buyya, Rajkumar China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China MNR China Polytech Ctr Terr Spatial Big Data Beijing Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3216 Australia Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Melbourne Vic Australia Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Performance-aware deployment of streaming applications is one of the key challenging problems in distributed stream computing systems. We proposed a performance-aware deployment framework (Pa-Stream) for distributed stream computing systems. By addressing the important aspects of the framework, this paper makes the following contributions: 1) investigated the performance-aware deployment of a streaming application over distributed and heterogeneous computing nodes, and provided a general application deployment model;2) demonstrated a streaming applications deployment scheme by proposing an artificial bee colony strategy that deploys application's vertices onto the best set of computing nodes;an incremental online redeployment strategy was used to redeploy the running application;3) developed and integrated Pa-Stream into Apache Storm platform;4) evaluated the fulfilment of low latency and high throughput objectives in a distributed stream computing environment. Experimental results demonstrate that the proposed Pa-Stream provided effective performance improvements on latency, throughput and resource utilisation.

关键词： performance awareness application deployment stream computing artificial bee colony algorithm distributed system

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Minimization of test time in system on chip using artificial intelligence-based test scheduling techniques

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NEURAL COMPUTING & APPLICATIONS 2020年第9期32卷 5303-5312页

作者： Chandrasekaran, Gokul Periyasamy, Sakthivel Rajamanickam, Karthikeyan Panjappagounder' Velalar Coll Engn & Technol Dept EEE Erode India Anna Univ Dept ECE Chennai Tamil Nadu India

System on chip (SoC) is a microchip which integrates many semiconductor devices into a single chip. The complete system that is integrated with many components and circuits has to be tested for its performance. At the same time, testing of SoC should not affect the final cost of the chip. The production cost of each and every chip can be reduced by minimizing the test time of each SoC. The testing time of each SoC can be minimized by using test scheduling techniques more efficiently and effectively. In this paper, artificial intelligence-based natural-inspired techniques such as ACO, MACO, ABC, bat and firefly algorithms are proposed to perform effective test scheduling, thereby reducing the total cost of the chip. The proposed algorithms are implemented on d695 and p22810 benchmark circuits for various values of TAM widths. The performance of the various algorithms was evaluated, and it is inferred that among the several algorithms used bat algorithm performs much better in reducing the overall testing time of SoC, and hence, the SoC cost is also reduced.

关键词： artificial intelligence Test scheduling Ant colony optimization artificial bee colony algorithm Bat algorithm Firefly algorithm

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Adaptive Block-Based Compressed Sensing Based on Pre-sampling Using LDPC matrices

Adaptive Block-Based Compressed Sensing Based on Pre-samplin...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Zhuyuan Xie Junkai Xia Mengjuan Ma School of Information Science and Engineering Yunnan University Kunming China

Assessing the sparsity of image blocks is the key of adaptive block-based compressed sensing (ABCS) algorithms. However, in practice, the digitized image cannot be directly obtained at the sampling device, therefore it is difficult to evaluate the sparsity of the original image. To solve this problem, two different measurement matrices are used in this paper. The original image is pre-sampled using binary low-density parity-check (LDPC) matrices, the sparsity of an image block is estimated by observing the pre-sampled measurement results, the total sampling rate is estimated according to the mean of the variances of the pre-sampled measurement results of all blocks. Then, a Gaussian random measurement matrix is used for the actual sampling. Experiment results show that, compared with the latest adaptive block-based compressed sensing methods, the proposed strategies have a significant improvement in peak signal-to-noise ratio (PSNR) at the same sampling rate, and it can also adaptively change the sampling rate according to the sparsity of the image.

关键词： Technological innovation PSNR Image coding artificial bee colony algorithm Parity check codes Sensors Compressed sensing

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A Comparative Analysis of Swarm Intelligent algorithms

A Comparative Analysis of Swarm Intelligent Algorithms

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Computer Science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Emmenual Reddy Ravneil Nand School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Fiji

A Black box program is one where the user cannot see the inner workings or one which has no side effects and the function of which need not be examined. Nature-inspired computing (NIC) or bio-inspired computing focuses on designing and developing computer algorithms and models using principles adopted from biology. Swarm intelligent algorithms (SIA) are bio-inspired algorithms developed based on the interactions between living organisms such as flocks of birds, ants, and fish. Swarm intelligence (SI) is one of the computational intelligence techniques used to solve complex problems. It is a reliable technique that has been used for treating several optimization problems. The two Swarm intelligent algorithms used for this research are particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm. The corresponding modified guided algorithms PSO-Guided and ABC-Guided are proposed. The proposed guided approach utilizes the current global best solution to guide the algorithm out of the local minima. The study finds that the guided methods outperform the standard algorithms in finding the best solution.

关键词： Computer science Computational modeling Benchmark testing artificial bee colony algorithm Fish Data engineering Reliability

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An Improved ABC algorithm and Its Application on Parameter Identification of Solar Cell Model

An Improved ABC Algorithm and Its Application on Parameter I...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Jingqing Jiang Liyan Xu Chuyi Song Haijie Bao College of Mathematics and Physics Inner Mongolia Minzu University Tongliao China College of Computer Science and Technology Inner Mongolia Minzu University Tongliao China Tongliao Horqin Experimental Senior Middle School Tongliao China Zhaluteqi First Middle School Tongliao China

ISBN: (纸本)9781665456173

In order to enhance the searching ability, an improved artificial bee colony algorithm (IABC) is proposed. In the employed bee stage, the global optimal honey source is introduced to guide the neighborhood search, the reversed learning mechanism is also introduced to increase the diversity of the population and the MR parameters is used to control the numbers of updating dimension. In the onlooker bee stage, quantum behavior is introduced to improve the convergent speed and accuracy of the algorithm. IABC is applied to solar cell model parameter identification. The experimental results show that the performance of parameter identification is improved by IABC. IABC provides a new method for solar cell parameter identification.

关键词： Learning systems Parameter estimation Photovoltaic cells Sociology artificial bee colony algorithm Silicon Behavioral sciences

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Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA

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Journal of Management Analytics 2020年第4期7卷 624-648页

作者： A.Kullaya Swamy B.Sarojamma S.G.Govt.Degree&PG College PilerIndia Department of Statistics Sri Venkateswara UniversityTirupatiIndia

The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous *** order to achieve efficient modeling of bank data transactions,Deep Belief Network(DBN)and Neural network(NN)classifiers are used in this ***,the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical ***,the extracted data are modeled using the combination of DBN and NN models,where the average modeled output from both the network is considered as the final *** above procedure is utilized for the two prediction models such as transaction count and transaction ***,the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average(ARIMA)model to compute the relationship between the transition count and transition ***,as the main contribution,the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with Lion algorithm(LA),and artificial bee colony(ABC)named L-ABC *** average of entire transactional amounts,*** modeled outputs are matched with the actual data to validate the performance of the implemented model.

关键词： bank data transaction time series modeling Deep Belief Network Neural Network Lion algorithm artificial bee colony algorithm

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Improved bee colony algorithm and Its Application in Optimization of Thermal Expansion Coefficient

Improved Bee Colony Algorithm and Its Application in Optimiz...

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Chinese Control and Decision Conference, CCDC

作者： Jinxiang Pian Alkasim Usman Abdulmajid MasumaRifah Tamanna Liangliang Sun School of Information and Control Engineering Shenyang Jianzhu University Shenyang City

ISBN: (纸本)9781665478977

The thermal deformation of the electric spindle is one of the key factors affecting the precision of machine tools, and the thermal expansion coefficient is a key parameter that affects the thermal deformation of the electric spindle. The coefficient of thermal expansion is affected by a variety of parameters, showing a strong nonlinear relationship, and there is no precise mathematical model to describe it. In this paper, the artificial bee colony algorithm does not rely on the advantages of precise mathematical model optimization, and the key coefficient in the thermal deformation model of the electric spindle—the thermal expansion coefficient is optimized, and then a thermal deformation model of the electric spindle based on the improved artificial bee colony algorithm is proposed. The simulation experiment results show that the improved bee colony algorithm has improved the optimization accuracy by about 2 times compared with the standard bee colony algorithm, the number of iterations to reach the convergence accuracy is reduced by 10%-16%, and the dynamic optimization capability is strengthened. With the 100MD60Y4 electric spindle as the controlled object, the thermal deformation of the electric spindle at different speeds is studied. Experimental results show that, at a rotational speed of 2000rad I s, for example, based on the amount of thermal experience thermal expansion coefficient and the obtained actually measured thermal deformation of the X, the Y, the Z error in three directions were 0.17m, 0.47m, 1.48μm, the error between the thermal deformation based on the optimized thermal expansion coefficient and the actual measured thermal deformation in the X, Y, and Z directions is 0.02μm, 0.13μm, and 0.56μm, respectively.

关键词： Deformable models Thermal expansion Heuristic algorithms Measurement uncertainty artificial bee colony algorithm Mathematical models Standards

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Improved bee colony algorithm and Its Application in Optimization of Thermal Expansion Coefficient

Improved Bee Colony Algorithm and Its Application in Optimiz...

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第34届中国控制与决策会议

作者： Jinxiang Pian Alkasim Usman Abdulmajid Masuma Rifah Tamanna Liangliang Sun School of Information and Control Engineering Shenyang Jianzhu University

The thermal deformation of the electric spindle is one of the key factors affecting the precision of machine tools,and the thermal expansion coefficient is a key parameter that affects the thermal deformation of the electric *** coefficient of thermal expansion is affected by a variety of parameters,showing a strong nonlinear relationship,and there is no precise mathematical model to describe *** this paper,the artificial bee colony algorithm does not rely on the advantages of precise mathematical model optimization,and the key coefficient in the thermal deformation model of the electric spindle—the thermal expansion coefficient is optimized,and then a thermal deformation model of the electric spindle based on the improved artificial bee colony algorithm is *** simulation experiment results show that the improved bee colony algorithm has improved the optimization accuracy by about 2 times compared with the standard bee colony algorithm,the number of iterations to reach the convergence accuracy is reduced by 10%-16%, and the dynamic optimization capability is *** the 100 MD60 Y4 electric spindle as the controlled object,the thermal deformation of the electric spindle at different speeds is *** results show that,at a rotational speed of 2000 rad/s,for example,based on the amount of thermal experience thermal expansion coefficient and the obtained actually measured thermal deformation of the X,the Y,the Z error in three directions were 0.17 m,0.47 m,1.48μm,the error between the thermal deformation based on the optimized thermal expansion coefficient and the actual measured thermal deformation in the X,Y,and Z directions is 0.02μm,0.13μm,and 0.56μm,respectively.

关键词： Electric Spindle Thermal Expansion Coefficient Thermal Deformation artificial bee colony algorithm

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