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Voltage Feed-Forward Control of Photovoltaic- Battery DC Microgrid Based on Improved seeker optimization algorithm

IEEE ACCESS 2024年 12卷 46067-46080页

作者： Wu, Aihua Gong, Rui Mao, Jingfeng Yu, Xiuyong He, Jianjun Li, E'xiang Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China Jiangsu Hantong Grp Nantong 226019 Peoples R China

The photovoltaic-battery DC microgrid is a new type of power system supply architecture that can effectively utilize renewable energy and is suitable for modern DC electrical equipment. In this paper, a fast and efficient maximum power point tracking (MPPT) photovoltaic (PV) control method and a battery energy storage system (BESS) bus control method are proposed to improve the PV utilization and the bus voltage performance. Firstly, the principle of photovoltaic-battery and power balance is analyzed, and the mathematical model of each distributed generation in the DC microgrid is derived. Secondly, by introducing the voltage increment and time-varying smoothing factor, the exponential variable step perturbation and observation method for PV controller is proposed to accelerate the MPPT process. Considering the intermittent disturbance of PV energy absorption and large power fluctuation on the DC bus, parameters of BESS voltage controller are optimized by the improved seeker optimization algorithm (ISOA) which is improved by the variational Cauchy operator and chaotic initialization optimization strategy. Furthermore, to improve the voltage closed-loop response speed and reduce the hysteresis characteristics, a feed-forward compensation strategy is designed. Finally, multi-scheme simulation analyses are implemented in MATLAB/Simulink. Compared with the simulation results of traditional control method, the proposed method reduces the average voltage ripple percentage from 3% to 1% and improves the MPPT response speed from 70ms to 10ms. The simulation results verified the correctness and effectiveness of the proposed method.

关键词： DC microgrid voltage stabilization control PID control seeker optimization algorithm maximum power point tracking

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A Multi-Strategy seeker optimization algorithm for optimization Constrained Engineering Problems

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IEEE ACCESS 2022年 10卷 7165-7195页

作者： Duan, Shaomi Luo, Huilong Liu, Haipeng Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

This paper proposes a multi-strategy seeker optimization algorithm (MSSOA) for optimization constrained engineering problems. In this paper, three strategies were adopted to improve the poor searching capability of the seeker optimization algorithm (SOA). The first strategy was triple black hole system capture to solve the local optima issue. The second and third strategies were the multi-dimensional random interference and the precocious interference to balance the exploration and exploitation processes. These three strategies are proposed to improve respectively the SOA algorithm, and compared with the three strategies to improve together the SOA algorithm for optimizing 15 benchmark functions;the way these three strategies work together is called multi-strategy;and the efficiency of the multi-strategy is illustrated the numerical optimizing results and the convergence curves, population's positions with iterations and the search history of the benchmark functions. The proposed multi-strategy method achieved better performance in optimizing of the benchmark functions compared to other six optimization methods. The numerical and experimental results analysis were observed with respect to the optimal solution curve, the convergence curve of the fitness function, the ANOVA tests, the calculation complexity of the algorithm, the running time of the algorithm routine, the exploration and exploitation capability, the Wilcoxon's rank-sum test, the performance profile of algorithm. The results showed that the proposed multi-strategy method was efficient in the benchmark functions. The proposed multi-strategy method also achieved better performance in optimizing of the engineering problems and provided better solutions compared to other six optimization methods.

关键词： optimization Interference Benchmark testing Convergence Heuristic algorithms Mathematical models Approximation algorithms seeker optimization algorithm triple black hole system capture multi-dimensional random interference precocious interference constrained engineering optimization problems

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The control design of transverse interconnected electronic control air suspension based on seeker optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2021年第8期235卷 2200-2211页

作者： Cao, Kai Li, Zhiqiang Gu, Yili Zhang, Liuyang Chen, Liqing Anhui Agr Univ Coll Engn 130 Changjiang West Rd Hefei 230036 Peoples R China

In this paper, in the light of the problems of the traditional air suspension PID controller in the process of body height adjustment, such as the adjustment time is too long, the overshoot phenomenon is obvious, and the control parameters cannot be adjusted in real time, a PID transverse interconnected electronic control air suspension(TIECAS) system controller based on seeker optimization algorithm (SOA) is designed, the proportion factor of PID is optimized by crowd search algorithm and get the optimal solution of PID controller parameters. The control system model is built in Matlab/Simulink simulation software. The simulation results show that the PID lateral interconnected air suspension controller based on SOA has faster response and avoids overshoot than the traditional PID controller. The control system was tested on a self-developed test vehicle with TIECAS structure. The test results show that the root mean square(RMS) values of the roll angle and pitch angle of the test vehicle are reduced from 0.024rad and 0.018rad before control to 0.019rad and 0.012rad, respectively, by 27.0% and 35.8%. The RMS values of the vertical acceleration of the center of mass after control are reduced by 27.6% and 42.4% compared with that without control, effectively improve the ride comfort and operation stability of the vehicle, The research results provide a new idea for the control of the vehicle transverse interconnected electronic air suspension system.

关键词： seeker optimization algorithm electronically controlled air suspension transverse interconnected vehicle attitude rapid control prototype

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optimization of Coupling Efficiency in Butterfly Optical Communication Laser Based on Chaotic Adaptive seeker optimization algorithm

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MICROMACHINES 2023年第7期14卷 1417-1417页

作者： Zhong, Shunshun Xu, Cong Sun, Dongmei Duan, Lian Duan, Ji-an Cent South Univ Coll Mech & Elect Engn State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Changsha Aeronaut Vocat & Tech Coll Changsha 410124 Peoples R China

A chaotic adaptive seeker optimization algorithm (CASOA) is proposed in this study to improve the coupling efficiency and accuracy of a butterfly optical communication laser. It primarily relies on chaotic disturbance to improve seeker search performance. The chaotic disturbance enables the algorithm to jump out from local extremes. Furthermore, chaos is associated with a novel strategy for optimizing search paths with a small population. A simulation and experiment are conducted to demonstrate that the CASOA with a few seekers has an excellent search success rate with few iterations in the coupling alignment. These results indicate that the proposed CASOA can reliably improve the coupling accuracy and efficiency of laser diodes and single-mode fibers.

关键词： coupling efficiency chaotic disturbance seeker optimization algorithm multi-degree of freedom alignment

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Combining Artificial Neural Network and seeker optimization algorithm for Predicting Compression Capacity of Concrete-Filled Steel Tube Columns

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BUILDINGS 2023年第2期13卷 391页

作者： Hu, Pan Aghajanirefah, Hamidreza Anvari, Arsalan Nehdi, Moncef L. L. Tech Univ Munich Sch Civil & Architectural Engn D-80333 Munich Germany Wuhan Municipal Construct Grp Co Ltd Wuhan 430023 Peoples R China Islamic Azad Univ Fac Engn Dept Civil Engn Qazvin Branch Qazvin *** Iran Islamic Azad Univ Fac Civil Engn Sci & Res Branch Engn & Management Tehran *** Iran McMaster Univ Dept Civil Engn Hamilton ON L8S 4L8 Canada

Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study proposes a novel methodology based on computational intelligence for estimating the compression capacity of CCFST. Accordingly, a conventional artificial neural network (ANN) is hybridized with a metaheuristic algorithm called the seeker optimization algorithm (SOA). Utilizing information such as the column's length, compressive strength of ultra-high-strength concrete, and the diameter, thickness, yield stress, and ultimate stress of the steel tube, the capacity of the column is predicted through non-linear calculations. In addition to the SOA, the future search algorithm (FSA) and social ski driver (SSD) are used as comparative benchmarks. The prediction results showed that the SOA-ANN can learn and predict the compression capacity pattern with high accuracy (relative error < 2.5% and correlation > 0.99). Also, this model outperformed both benchmark hybrids (i.e., FSA-ANN and SSD-ANN). Apart from accuracy, the configuration of the SOA-ANN is simpler owing to the smaller population recruited for the optimization task. An explicit formula for the proposed model is developed, which, owing to its observed efficiency, can be reliably applied to CCFST columns for the early estimation of the compression capacity.

关键词： CCFST columns axial compression capacity composite structures neural network seeker optimization algorithm

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Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

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MECHATRONICS 2022年 88卷

作者： Xing, Xiangrui Ding, Hongwei Liang, Zhuguan Li, Bo Yang, Zhijun Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Peoples R China Yunnan Educ Dept Kunming 650500 Peoples R China

Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning.

关键词： seeker optimization algorithm Advantage actor-critic Path planning Path de-redundancy Deep reinforcement learning

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A State Feedback Controller for PMSMs Based on Penalty Term Augmented seeker optimization algorithm 22

A State Feedback Controller for PMSMs Based on Penalty Term ...

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22nd International Conference on Electrical Machines and Systems (ICEMS)

作者： Hu, Changchang Sun, Xiaodong Yang, Zebin Lei, Gang Guo, Youguang Zhu, Jianguo Jiangsu Univ Automot Engn Res Inst Zhenjiang Jiangsu Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia

ISBN: (纸本)9781728133980

This paper presents the design of a state feedback controller (SFC) for permanent-magnet synchronous motor (PMSM) drive. First, two integrals, the integral of rotor speed error and d-axis current error are added into the discretized state space model of PMSM to eliminate steady-state error in speed and id. Then, the seeker optimization algorithm (SOA) is employed to get the parameters of the proposed SFC. Also, in order to avoid overshoots in speed tracking, a penalty term is added to the fitness function. Finally, the SOA based SFC with and without the penalty term are compared in experiments.

关键词： Permanent magnet (PM) synchronous motor PM hub motor state feedback controller seeker optimization algorithm

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Fractional-order controller design in frequency domain using an improved nonlinear adaptive seeker optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第5期25卷 4299-4310页

作者： Kumar, Mano Ranjan Deepak, Vishwa Ghosh, Subhojit Natl Inst Technol Dept Elect Engn Raipur Madhya Pradesh India

Two nonlinear adaptive versions of the conventional seeker optimization algorithm (SOA) have been proposed for the design of fractional-order controllers using frequency domain specifications. The highly nonlinear and undetermined nature of equations resulting from controller design specifications rules out obtaining a closed-form solution. In this regard, the controller design task has been formulated as an optimization problem and solved using modified variants of the SOA. With the nonlinear adaptation of tuning parameters, the proposed variants of the SOA increase the probability of finding global optima along with improved convergence speed. This has been achieved by incorporating exponential weighting and chaotic behavior in the search process. The validation of the proposed techniques on a set of fractional-order controller design problems clearly exhibits their superiority over other algorithms and controller design techniques. The hardware implementation of the controllers on a DSP TMS320F2812 board ensures applicability for real-time applications.

关键词： seeker optimization algorithm exponential weighting chaotic search fractional-order controller gain margin and phase margin

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seeker optimization algorithm for optimal control of manipulator

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2016年第6期43卷 677-686页

作者： Chen, Chunchao Li, Jinsong Luo, Jun Xie, Shaorong Li, Hengyu Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Henan Peoples R China State Nucl Power Plant Serv Co Shanghai Peoples R China

Purpose - This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator. Design/methodology/approach - In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller. Findings - Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness. Originality/value - A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.

关键词： Fuzzy PID Robot manipulator seeker optimization algorithm

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Identification of Fractional Order Circuits from Frequency Response Data Using seeker optimization algorithm

Identification of Fractional Order Circuits from Frequency R...

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International Conference on Industrial Instrumentation and Control

作者： Kumar, Mano Ranjan Ghosh, Subhojit Das, Shantanu Natl Inst Technol Raipur Dept Elect Engn Raipur Madhya Pradesh India BARC Reactor Control Div Bombay Maharashtra India

ISBN: (纸本)9781479971657

Linear circuits and systems are generally described by traditional differential equations and integer order transfer functions based on the assumption that the dynamics are lumped and time invariant. However, as compared to the conventional integer order calculus, many dynamical systems are better represented by fractional calculus with interaction among the variables modelled by fractional integration and/or fractional differentiation. The present work proposes a generalized approach for the identification of fractional order systems in frequency domain using experimental data. To achieve the same, the system identification task has been framed as an optimization problem and solved using seeker optimization algorithm. The algorithm seeks to attain a set of system parameters for which the deviation between the simulated response of the identified system and experimental data is minimized. The proposed approach has been validated on a set of electrical circuits with varying configuration. The simulation and experimental results reveals that all of the test circuits are better represented by fractional order model, over a wide range of frequency.

关键词： Fractional Order seeker optimization algorithm Frequency Response Data FO Circuit Identification

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