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Robust adaptive control of a bio-inspired robot manipulator using bat algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2016年 56卷 164-176页

作者： Rahmani, Mehran Ghanbari, Ahmad Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran Univ Tabriz Sch Engn Emerging Technol Mechatron Lab Tabriz Iran

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Caterpillar robot Fractional PID controller SMC Robust control Adaptive control bat algorithm

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Coordinated design of fuzzy supplementary controllers for generator and STATCOM voltage regulators using bat algorithm optimization

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第9期26卷 1847-1862页

作者： Ramirez-Gonzalez, Miguel Castellanos-Bustamante, Rafael Guillermo Calderon-Guizar, J. Malik, Om. P. Inst Invest Elect Reforma 113 Cuernavaca 62490 Morelos Mexico Univ Calgary Dept Elect & Comp Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

A coordinated design of power system stabilizer and power oscillation damping controller for generator and STATCOM voltage regulators is presented. Because fuzzy logic has the potential to overcome the inherent limitations of conventional and analytical model-based design approaches, simple fuzzy logic controllers operating as power system stabilizer and power oscillation damping controller are used in this work to enhance power system stability. The coordinated design of these fuzzy supplementary controllers (FSCs) is formulated as a constrained optimization problem with a time-domain based cost function. The optimal set of controller parameters is determined by applying a simultaneous tuning approach based on bat algorithm optimization. Simulations of a two-area power system under several operating conditions and perturbations are carried out to validate the effectiveness of the proposed alternative in damping system oscillations. Performance of the proposed coordinated FSCs is compared with other coordinated and uncoordinated design approaches, including coordinated FSCs using genetic algorithm and particle swarm optimization. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： bat algorithm fuzzy controller power system stabilizer STATCOM power oscillation damping controller power system stability

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Multiobjective Design Optimization of Stator for Synchronous Generator Using bat algorithm and Analysis of Magnetic Flux Density Distribution

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第9-10期49卷 919-929页

作者： Karaoglan, Aslan Deniz Perin, Deniz Yilmaz, Kemal Balikesir Univ Dept Ind Engn TR-10145 Balikesir Turkey ISBIR Elect Co Dept R&D Balikesir Turkey

In this study, we aimed to optimize 3000 kVA synchronous generator (SG) stator design to obtain the desired magnetic flux density distribution and efficiency. We used Maxwell simulations for experiments on some design parameters of stator (slot height and teeth width). Then second-order regression models are calculated that represent the relations between the factors (design parameters) and the measured performance criteria (called as the responses: stator-teeth flux density, stator-yoke flux density, and efficiency). These regression models are used at the multiobjective optimization phase. bat algorithm (BA) is used for performing the multiobjective optimization. By combining Maxwell with regression modeling and BA, the efficiency of the SG is increased to 96.84% from 96.5% with a more acceptable magnetic flux density (between 1.65 and 1.70 T ranges). The stator-teeth flux density and statoryoke flux density are calculated as 1.9 T and 2.07 T for the current SG, whereas these values are reduced to 1.647 and 1,634 T, respectively, for the optimized SG. Results of this study show how the numerical simulation can be successfully combined with the BA to improve the efficiency of the SG by providing the desired magnetic flux density distribution.

关键词： synchronous generator stator design Maxwell simulation regression modeling nature inspired algorithms swarm-based meta-heuristic bat algorithm multiobjective optimization magnetic flux density distributions efficiency

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Test Scheduling and Test Time Minimization of System-on-Chip Using Modified bat algorithm

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IEEE ACCESS 2022年 10卷 126199-126216页

作者： Chandrasekaran, Gokul Kumar, Neelam Sanjeev Karthikeyan, P. R. Vanchinathan, K. Priyadarshi, Neeraj Twala, Bhekisipho Velalar Coll Engn & Technol Dept Elect & Elect Engn Erode 638012 India SIMATS Saveetha Sch Engn Dept Biomed Engn Chennai 602105 India SIMATS Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 India JIS Coll Engn Dept Elect Engn Kolkata 741235 India Tshwane Univ Technol Digital Transformat Portfolio ZA-0183 Pretoria South Africa

System-on-Chip (SoC) is a structure in which semiconductor components are integrated into a single die. As a result, testing time should be reduced to achieve a low cost for each chip. Effective test scheduling can reduce the SoC testing time, which is more challenging due to its complexity. In this paper, the modified bat algorithm-based test scheduling is proposed. Testing is carried out on the SoC ITC'02 benchmark circuits. The Modified bat method is a recently heuristic algorithm that performs global optimization by imitating bat echolocation. Compared to other state-of-the-art algorithms, the Modified bat Optimization method reduces testing time on SoCs. This paper improves the algorithm's exploration process by adjusting the equation for bat loudness (A(0)) and pulse emission rate (r). The modified bat algorithm converges to the optimal solution faster. It has been used in 14 international standard test functions. The test results indicate that the modified bat algorithm has a fast convergence speed, which minimizes the testing time compared to other evolutionary algorithms on the ITC'02 SoC benchmark circuits.

关键词： Optimization Benchmark testing Convergence Scheduling Genetic algorithms Heuristic algorithms System-on-chip Test time system-on-chip modified bat algorithm bat algorithm test scheduling

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Application of improved bat algorithm in optimal power flow problem

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APPLIED INTELLIGENCE 2018年第8期48卷 2304-2314页

作者： Yuan, Yanbin Wu, Xiaotao Wang, Pengtao Yuan, Xiaohui Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

This paper proposes an improved bat algorithm to solve multi-objective optimal power flow problem (MOPF) based on the weighted method. The MOPF problem is formulated as a non-linear constrained optimization problem where two objective functions (minimization of fuel cost and emission) and various constraints are considered. After having found the Pareto solutions with the improved bat algorithm, the fuzzy set theory is used to find the compromise solution. Finally, the IEEE 57-bus system is applied to verify the performance of the proposed method for the MOPF problem. The results are compared with those obtained by the state-of-the-art optimization algorithms reported in literature. The simulation results demonstrate the superiority of the proposed method for solving the MOPF problem in terms of solution quality.

关键词： bat algorithm Multi-objective optimization Optimal power flow Fuzzy logic

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Six Phase Transmission Line Protection Using bat algorithm Tuned Stacked Sparse Autoencoder

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第2期51卷 113-130页

作者： Rao Althi, Tirupathi Koley, Ebha Ghosh, Subhojit Shukla, Sunil Kumar Natl Inst Technol Raipur Elect Engn Dept Raipur India Nirma Univ Inst Technol Dept Elect Engn Ahmadabad India MITS Gwalior Dept Informat Technol Gwalior India

Six-phase transmission lines have the capability to address the continually evolving power demand. It allows upgrading the power transfer capability of the prevailing three-phase double circuit line without major changes in the transmission corridors. However, the operational performance of any six-phase system is highly dependent on its protection scheme. The possibility of larger number of faults in six-phase system complicates the protection task. Furthermore, the harmonics intrusion arising because of nonlinear loading compromises the reliability of the conventional threshold-based protection schemes. In this regard, this article addresses the above-mentioned challenges by developing a protection scheme based on the hybrid frameworks of bat algorithm and stacked sparse autoencoder-deep neural network (SAE-DNN). To overcome the limitation of SAE-DNN regarding optimal selection of architecture and tuning parameters, the selection task has been formulated as an optimization problem and solved using bat algorithm. The use of raw voltage and current signals as input to the SAE-DNN reduces the overall complexity of the protection scheme. The efficacy of the proposed scheme has been validated for all 120 types of faults under varying operating, loading and fault scenarios. Furthermore, the proposed scheme has been validated for practical settings by performing real-time simulations on OPAL-RT digital simulator.

关键词： bat algorithm deep neural network six phase transmission line protection stacked sparse autoencoder real time validation

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Planning the sports training sessions with the bat algorithm

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NEUROCOMPUTING 2015年第PartB期149卷 993-1002页

作者： Fister, Iztok Rauter, Samo Yang, Xin-She Ljubic, Karin Fister, Iztok, Jr. Univ Maribor Fac Elect Engn & Comp Sci Maribor 2000 Slovenia Univ Ljubljana Fac Sport Ljubljana 1000 Slovenia Middlesex Univ Sch Sci & Technol London N17 8HR England Univ Maribor Fac Med Maribor 2000 Slovenia

Planning proper sports training has always been a very challenging task for coaches. In line with this, they need to have almost two special abilities: firstly, to have a lot of earlier experiences with sports training and secondly, to know the capability of their athletes very well. New ways in planning sports training have emerged with development of pervasive and mobile technologies. Recently, a GPS receiver is one of the most useful parts of each standard sports watch that enables athletes to track the duration of their sports activities and analyze them later on digital computers using GPS viewers. Most sport's watches are also capable of measuring an athlete's heart rate during activities. Both measures represent reliable data sources that can be used for planning the sports trainings by coaches. In this paper, we introduce a novel intelligent planning method for sports training sessions, where the training plans are generated on digital computers using the bat algorithm according to reliable data obtained from sports watches. Real-world experiments showed promising results that encouraged us to proceed with this research also in the future. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Sport training Training load bat algorithm Swarm intelligence

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Optimal placement of steel plate shear walls for steel frames by bat algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2015年第1期24卷 1-18页

作者： Gholizadeh, Saeed Shahrezaei, Amir Masoud Urmia Univ Dept Civil Engn Orumiyeh Iran Islamic Azad Univ Sci & Res Branch Dept Civil Engn Orumiyeh Iran

Layout optimization of steel frames with steel plate walls (SPWs) using a meta-heuristic search algorithm is the main aim of the present study. SPWs are lateral load-resisting systems, especially against earthquake excitation. These systems offer significant advantages in terms of cost, performance and ease of design compared with other systems. In this study, orthotropic membrane model is used to model the behaviour of steel plate shear walls. The newly developed bat algorithm, which is based on the echolocation behaviour of bats, is employed as the present study optimizer. Design variables of the optimization problem consist of the cross sections of beams and columns of the frame, the web plate thicknesses of SPWs and the placement of SPW in the frame. The bat algorithm performs suitable selection of sections from the AISC wide-flange (W) shapes list. Strength constraints of the American Institute of Steel Construction Load and Resistance Factor Design and displacement constraints are checked during the optimization process. The results reveal the effectiveness of the proposed method for optimization of steel frames with SPWs. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： steel frame steel plate shear walls optimum design layout optimization bat algorithm meta-heuristic

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Thermal positioning error modeling of machine tools using a bat algorithm-based back propagation neural network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期97卷 2575-2586页

作者： Li, Yang Zhao, Ji Ji, Shijun Jilin Univ Sch Mech Sci & Engn Changchun 130025 Jilin Peoples R China

Thermal error of a machine tool is one of the main reasons affecting the machining accuracy. Heat production and heat transfer of a machine tool are too complicated to predict the generated thermal error accurately. According to the nonlinear and time-varying characteristics of thermal error, the back propagation (BP) neural network is perfectly suitable for thermal error modeling, which has been extensively used to map the nonlinear relationship. However, traditional BP neural network usually has poor prediction performance under different operating conditions. Therefore, a new swarm intelligent optimization algorithm, bat algorithm (BA), is introduced to optimize BP neural network and improve its performance. The focus of this paper is the application of the combined algorithm (bat algorithm-based back propagation neural network) to solve the problem of thermal error modeling. Thermal positioning error experiments were conducted on a three-axis experiment bench. The experimental results show that thermal positioning error model built by BA-BP neural network is more stable and has high prediction accuracy and strong robustness, which can provide a candidate method for thermal error modeling.

关键词： Thermal error modeling bat algorithm BP neural network CNC machine tools Swarm intelligent optimization algorithm

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A new bat algorithm based on iterative local search and stochastic inertia weight

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 104卷 202-212页

作者： Gan, Chao Cao, Weihua Wu, Min Chen, Xin China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

bat algorithm (BA) is a heuristic optimization algorithm based on swarm intelligence and the inspiration from the nature behavior of bats. It has some advantages including fast solving speed, high precision and only few parameters need to be adjusted. However, BA is easy to fall into local optima and has unstable optimization results due to low global exploration ability. In order to overcome these weakness, a new bat algorithm based on iterative local search and stochastic inertia weight (ILSSIWBA) is proposed in this paper. A kind of local search algorithm, called iterative local search (ILS) is introduced into the proposed algorithm. The ILS algorithm disturbs the local optimum and do some local re-search, so that the ILSSIWBA has strong ability to jump out of the local optima. In addition, a weight updating method, called stochastic inertia weight (SIW) is also introduced into the proposed algorithm. Considering the SIW in the velocity updating equation can enhance the diversity and flexibility of bat population, so that the ILSSIWBA has stable optimization results. Meanwhile, the pulse rate and loudness are modified to enhance the balance performance between global and local search. Moreover, the global convergence of ILSSIWBA is proved by the convergence criteria of stochastic algorithm. In the end, the ILSSIWBA is compared with directional bat algorithm (DBA) and other algorithms on 10 classic benchmark functions, CEC 2005 benchmark suite, and two real-world problems. The results show that ILSSIWBA has remarkable advantages in optimization accuracy, solving speed and convergence stability. This algorithm lays a solid foundation for solving modeling, optimization and control problems of complex systems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： bat algorithm Iterated local search Stochastic inertia weight Swarm intelligence Global optimum

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