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Design and Optimization of FOPID Controller for Precise Power Head Control in Reverse Drilling Rigs Using gray wolf algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第6期34卷

作者： Zhang, Jun Guo, Jiahua Li, Ruibin Fu, Jiaxing Taiyuan Univ Technol Coll Mech & Engn Taiyuan 030024 Shanxi Peoples R China Middling Coal Grp Shanxi Co Ltd Taiyuan 030000 Shanxi Peoples R China Coal Sci Res Inst Co Ltd Intelligent Collaborat Ctr Beijing 100013 Peoples R China

Reverse well drilling rig is an important support for realizing deep mineral resource extraction, and the precise control law of its power head is crucial for improving drilling efficiency and reducing safety accidents. In challenging environments and varying rock strata distributions at different well depths, Proportional-Integral-Derivative (PID) controller is used to control the power head of the reverse drilling rig, despite the simple control, flexible and convenient adjustment, but due to the fact that there are only three parameters regulated by the PID controller, the control is not precise enough and the adjustment accuracy is not high enough, Overcoming the drawbacks of PID control technology at a fundamental level can be an extremely challenging task, which results in the failure of real-time feedback and precise control of the power head of the reverse drilling rig and precise control. The research is based on the theory of wellbore engineering machinery, fractionalorder control technology and intelligent optimization algorithm. Using a combination of physical modeling, mathematical modeling and simulation test analysis, the intelligent optimization algorithm (gray wolf Optimization (GWO)) is used to parameterize the fractional-order PID (FOPID) controller and apply it to the rotational speed control of power head. The energy conservation relationship is coupled to the kinetic mathematical model of the power head. Laplace transform analysis is utilized to derive the integer-order mathematical transfer function linking the power head speed and the electric displacement signal. The fractional-order mathematical transfer function is subsequently determined using the least square method. With the help of MATLAB-Simulink software, build a model of electro-hydraulic proportional control system for countershaft drilling rigs, adopt six intelligent optimization algorithms to adjust the parametric fractional-order controller, and examine the impact of step distur

关键词： Reverse drilling rig power head FOPID gray wolf algorithm motion control

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Design of CCM boost converter using fractional-order PID controller optimized with gray wolf algorithm for power factor correction

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第10期12卷 3685-3693页

作者： Lai, Songhong Wang, Wenjian Zhejiang Business Coll Dept Appl Engn Hangzhou 310053 Peoples R China Xidian Univ Sch Microelect Xian 710071 Peoples R China

The primary determining elements for the effective operation of power electronic converters are power quality (PQ) in terms of power factor, total harmonic distortion (THD), and ideal output regulation. The uncontrolled diode bridge rectifier draws pulsed input current, which may be managed with an easy and efficient active wave shaping control system in this study, bringing the power factor (PF) closer to unity and meeting the THD parameters. It controls the generated voltage as well. The goal of this study is to create a CCM boost converter-based active PFC control circuit that uses fractional-order PID control and gray wolf (GW) optimization that has higher robustness with lower sensitivity to error. The suggested plan uses a cascade controller providing adequate operation and results for upholding PQ. Additionally, the fractional notion has many benefits, including greater robustness to shocks and decreased sensitivity to inaccuracy. Simulation findings that guarantee significant tracking of the reference signal with a decreased rate of THD have been shown.

关键词： Power factor correction Total harmonic distortion Fractional-order PID controller gray wolf algorithm

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Optimal Path Planning for Mobile Robots in Complex Environments Based on the gray wolf algorithm and Self-Powered Sensors

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IEEE SENSORS JOURNAL 2023年第18期23卷 20756-20765页

作者： Zhang, Qian Zhao, Jian Pan, Lei Wu, Xu Hou, Yanyan Qi, Xiaoqian Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Art & Design Tianjin 300384 Peoples R China

The term "mobile robot" or "intelligent mobile robot" refers to an autonomous system with specific sensory skills that can move independently in an environment that contains obstacles and can perform certain activities and belongs to the category of intelligent robots. The use of mobile robots combines expertise from many different academic fields, including strongly integrated techniques in biology, anthropology, computer science, control theory, power transmission, measurement, and sensing. At present, there are already cases of using mobile robots to perform various simple tasks, but due to the low efficiency of optimal path planning, the scope of application cannot be further expanded. Therefore, based on the improved gray wolf algorithm (IGWA) and self-powered sensor technology, this article optimized the optimal path planning method and energy driving method of the mobile robot, so that the mobile robot can be applied in complex environments. The experiment compared the improved path planning algorithm in this article with the traditional path planning algorithm. The results show that the optimal path planning using the IGWA can search the random tree algorithm, the improved bee colony algorithm, and the multiobjective based on edge distribution estimation faster. Compared with the optimization algorithm, the optimization effect was increased by 14.84%. It is beneficial to the optimal path planning of mobile robots in complex environments.

关键词： gray wolf algorithm move robot optimal path planning self-powered sensors

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Optimal fuzzy controller design for industrial gear system by gray wolf algorithm

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第4期11卷 1856-1866页

作者： Masomi, Zynab Yaghoobi, Mahdi Kobravi, Hamid R. Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran

Chaotic behavior in gear system vibrations is deemed undesirable. Chaotic conduct must therefore be eliminated and controlled at all costs. This work proposes an optimal fuzzy controller to control the gear system's chaotic behavior. A gray wolf algorithm is used in this scheme to adjust the fuzzy membership functions and controller parameters. The simulation results in the presence and absence of noise were examined in order to evaluate this method. Finally, the proposed method is contrasted with other types of control methods. The results show that the proposed method outperformed similar methods in terms of mean square error and fluctuations in the goal of eliminating chaotic behavior.

关键词： Gear system Chaotic behaviors Fuzzy control gray wolf algorithm

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WSN node location based on beetle antennae search to improve the gray wolf algorithm

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WIRELESS NETWORKS 2022年第2期28卷 539-549页

作者： Yu, Xiu-wu Huang, Lu-ping Liu, Yong Zhang, Ke Li, Pei Li, Ying Univ South China Hengyang Peoples R China Hunan Engn Res Ctr Uranium Tailings Decommiss & T Hengyang Peoples R China Wuhan Univ Technol Wuhan Peoples R China

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved.

关键词： Wireless sensor network Beetle antennae search gray wolf algorithm Node positioning Good point set Intelligent optimization algorithm Positioning accuracy

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Temperature and humidity optimisation control of cold chain vehicle carriage based on gray wolf algorithm

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2022年第1-2期89卷 24-37页

作者： Wan, Yulong Li, Xinchun China Univ Min & Technol Sch Econ & Management Xuzhou 221116 Jiangsu Peoples R China Jiangsu Vocat Coll Elect & Informat Sch Business Huaian 223003 Jiangsu Peoples R China

There are some problems in the traditional temperature and humidity control of cold chain transportation vehicles, such as poor control effect, poor real-time control, etc. The paper introduces Descartes coordinate system to construct the air motion tensor model in the carriage of cold chain transportation vehicles. The influence parameters of temperature and humidity of cold chain transport vehicles are divided into different temperature and humidity parameters. The gray wolf algorithm is used to search for the optimal solution of humidity influence parameters, and different wolf fitness models are constructed to determine the optimal solution of parameters to realise optimal control. The comparison shows that: the control deviation of temperature and humidity of cold chain transport vehicle compartment is always lower than 0.4, and the control efficiency coefficient is higher than 0.9.

关键词： cold chain transport vehicle compartment temperature and humidity gray wolf algorithm tensor model temperature and humidity comprehensive parameters comfort index

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Optimization of Synchronous Control Parameters Based on Improved Sinusoidal gray wolf algorithm

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PROCESSES 2024年第10期12卷 2171页

作者： Wang, Taoyu Sun, Shiyan She, Bo Naval Univ Engn Ordnance Engn Coll Wuhan 430000 Peoples R China

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved gray wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.

关键词： improved sine algorithm gray wolf algorithm particle crossing parameter optimization

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Prediction model of dissolved oxygen in marine pasture based on hybrid gray wolf algorithm optimized support vector regression

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DESALINATION AND WATER TREATMENT 2021年 222卷 156-167页

作者： Yin, Baoan Wang, Rong Qi, Shengbo Yu, Jingdong Jiang, Wenliang Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Sencott Intelligent Instrument Co Ltd Qingdao 266100 Peoples R China

Water quality prediction plays a vital role in water pollution warning and control. However, traditional prediction models usually suffer from low efficiency and poor robustness. To predict accurately the dissolved oxygen concentration in the marine pasture, a dissolved oxygen prediction model, based on wavelet analysis and hybrid gray wolf algorithm (HGWO) optimized vector regression, was established. Because of the water quality data in the marine pasture is a stationary time series. To improve the accuracy of water quality data, wavelet analysis was applied for data pre-processing in this paper. Besides, after the gray wolf algorithm (GWO) was optimized by the differential evolution algorithm (DE), it was used to optimize the support vector regression (SVR). Hence, the SVR's disadvantages of optimization ability and prediction accuracy both were improved. Back propagation neural network (BPNN), SVR, GWO-SVR, DE-SVR, and this model were, respectively, used to predict the dissolved oxygen concentration of Beidaihe marine pasture. The experimental results show that the mean square error, mean absolute error, and average percentage error of the model are 0.1658, 0.359, and 0.0305, respectively, which are better than the traditional prediction model. So this model has higher prediction accuracy and stronger generalization ability, and it can provide a reference for the precise regulation of aquaculture.

关键词： Wavelet analysis gray wolf algorithm Differential evolution algorithm Support vector regression Dissolved oxygen

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Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm

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APPLIED THERMAL ENGINEERING 2023年第1期234卷

作者： Wang, Qiu-Yu Lv, Xian-Long Zeman, Abdol Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Sun life Co Elect Engn Dept Baku Azerbaijan

The growth of electricity consumption and demand for higher quality of electricity has directed the electricity industry towards using new technologies. In this paper, a novel model is proposed for the optimal scheduling of multi-energy microgrid (MEM) systems that rely on renewable energy sources. The model incorporates various emerging high-efficient technologies, including electric vehicle (EV) parking lots, power-to-gas (P2G) facilities, and demand response programs. The MEM system described in this paper comprises several components, such as wind energy generation, multi-carrier energy storage technologies, a boiler, a combined heat and power unit, power-to-gas (P2G) capability, electric vehicles (EVs), and demand response programs. The main objective of the system is to minimize the total operational cost. Moreover, the system operator has the opportunity to actively participate in the electricity, heat, and gas markets, enabling them to fulfill local energy demands and maximize profits through energy exchanges. The proposed system is exposed to uncertainties caused by wind power, demand, electric vehicles and power price. The optimization process in this study utilizes the gray wolf algorithm, which has been enhanced by incorporating the Local Escaping Operator method. This improved algorithm is employed to optimize the scheduling and operation of the multi-energy microgrid (MEM) system. The goal of the gray wolf algorithm, in combination with the Local Escaping Operator method, is to find optimal solutions that minimize the total operational costs of the MEM system while considering factors such as renewable energy generation, energy storage, demand response programs, and the integration of emerging technologies. The results obtained from the optimization process demonstrate a reduction in the total cost of operations when various technologies are integrated into the grid. By applying the proposed optimization model, which incorporates the gray wolf Algorith

关键词： Multi -energy microgrid Wind power Electric vehicle Load management Power to gas conversion gray wolf algorithm

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Research on Data Classification Method of Optimized Support Vector Machine Based on gray wolf algorithm

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INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE COMPUTING 2023年第2期15卷 1页

作者： Ma, Jinqiang Fan, Linchang Tian, Weijia Miao, Zhihong China Peoples Police Univ Beijing Peoples R China Chengdu Publ Secur Bur Chengdu Peoples R China

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.

关键词： data classification gray wolf algorithm Kernel function Support vector machine

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