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Research on the motion trajectory control of industrial manipulators by the incremental PID control optimization algorithm

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MECHANICS & INDUSTRY 2025年 26卷

作者： Wang, Jicai Chen, Fangyan Jiaxing Nanyang Polytech Inst Jiaxing 314031 Zhejiang Peoples R China

With the wide application of industrial manipulators, the research of its moving trajectory control method has become more and more important content. In this paper, the motor closed-loop control strategy of an industrial 6R manipulator was briefly introduced, the proportional, integral, and differential ( (PID) algorithm commonly used in motor control was analyzed, and its shortcomings were pointed out. Then, the incremental PID algorithm was introduced to design a grey wolf optimization (IGWO) algorithm to achieve parameter tuning. Simulation experiments were carried out in a MATLAB environment. The results showed that the incremental PID algorithm generated a smaller control deviation than the PID algorithm. The average deviation of the three axes of the incremental PID algorithm undergoing IGWO parameter tuning was less than 0.2 mm, and the moving time was between 1.1 s and 1.3 s. The control accuracy and efficiency of the incremental PID algorithm undergoing IGWO parameter tuning were better than those of the incremental PID algorithm whose parameters were tuned using the trial-and-error method and the grey wolf optimization algorithm respectively. Moreover, better stability and anti-interference were observed in step response and sinusoidal response. The results prove that the incremental PID algorithm combined with the IGWO algorithm is reliable in the moving trajectory control of industrial manipulators and can be applied in practice.

关键词： Incremental PID control industrial manipulator grey wolf optimization algorithm sine response

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Research on dynamic flatness feedback control strategy based on IGWO control efficiency identification for cold tandem rolling mill

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-21页

作者： Zhou, Xiaomin Li, Liqi Wang, Shuaikun Xiong, Qingxia Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Peoples R China

Flatness control is an important means to improve the quality of the strip, and modern tandem cold rolling mills are equipped with various control actuators. The existing flatness control efficiency is determined offline and remains constant throughout the manufacturing process. During actual manufacturing, equipment, and process states may change, making it necessary to identify flatness control efficiency online. The main flatness feedback control strategy is a fixed priority sequence in the existing CVC (Continuous Variable Crown) cold tandem rolling mill. The control system does not have the flexibility to adjust the sequence based on real-time flatness. Aiming at the above problems, in order to improve the effect of flatness control, this paper proposes a dynamic flatness feedback control strategy based on control efficiency identification. To address the issue of high cost and difficulty in updating the on-site flatness control efficiency, based on historical actual data, the IGWO (improved grey wolf optimization) algorithm is used to intelligently identify control efficiency. Data post-processing through IF (isolated forests) and CLT (central limit theorem) obtains results on control efficiency. At the same time, a similarity index is proposed to dynamically adjust the priority sequence of feedback control based on the identification results of the control efficiency and the measured flatness deviations. Finally, the field application results show that the flatness control accuracy is improved, the reduction value of the flatness deviations increased by 13.41%, and the reduction rate of the flatness deviations increased by 9.66%.

关键词： Flatness control efficiency grey wolf optimization algorithm Flatness feedback control Feedback control strategy

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A novel time-varying parameters structural adaptive Hausdorff fractional discrete grey model and its application in renewable energy production and consumption prediction

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ENERGY 2025年 318卷

作者： Wang, Yong Zhang, Zejia Wang, Yunhui Sun, Lang Yang, Rui He, Wenao Sapnken, Flavian Emmanuel Li, Hong-Li Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China Neijiang Normal Univ Key Lab Numer Simulat Sichuan Prov Univ Sch Math & Informat Sci Neijiang 641000 Sichuan Peoples R China Univ Douala Univ Inst Technol Lab Technol & Appl Sci POB 8698 Douala Cameroon Xinjiang Univ Coll Math & Syst Sci Urumqi 830017 Xinjiang Peoples R China

The role of energy is essential in driving human development and achieving economic and social progress. It's crucial to make precise predictions about how traditional and new energy sources will be used in the future, as this information can help a country create effective energy strategies and make adjustments to its energy structure accordingly. Considering the common presence of volatility, nonlinearity and periodicity in long-term energy data series, this paper proposes a novel time-varying parameters adaptive discrete grey prediction model FHATDGM (1,1) based on Hausdorff fractional derivative. This model further explores the possible periodic features in the system, in order to more comprehensively and accurately describe the development trend of the time series. In addition, the grey wolf optimization algorithm (GWO) is selected to optimize the parameters of the model. Then, for the robustness of the results obtained by introducing intelligent optimization algorithm, this paper uses the method of combining Monte Carlo simulation and probability density analysis to test, indicating that the proposed model has better stability and accuracy compared with the existing six models. Finally, three actual cases of China's solar power generation, United States hydroelectric power generation and China's consumption of coal are predicted. The results show that the FHATDGM (1,1) has the highest fitting and forecasting accuracy. The reduction in prediction error compared to the other six models ranged from 1.46 % to 37.49 %.

关键词： grey model grey wolf optimization algorithm Hausdorff fractional derivative Monte-carlo simulation Renewable energy production and consumption

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Robust design and best control channel selection of FACTs-based WADC for improving power system stability using grey wolf Optimizer

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ENERGY REPORTS 2024年 11卷 55-70页

作者： Davoudkhani, Iraj Faraji Shakarami, Mahmoud Rerza Abdelaziz, Almoataz Y. El-Shahat, Adel Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Lorestan Univ Dept Elect Engn Lorestan Iran Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Purdue Univ Sch Engn Technol W Lafayette IN 47907 USA

With recent advances in wide-area measurement systems (WAMS), wide-area damping controllers (WADCs) based on remote signals can effectively damp inter-area oscillations. However, these signals are inherently subjected to communication time-delay which may adversely affect the damping of inter area oscillations. The purpose of this paper is to present a optimization-based method for designing a WADC for a static synchronous series compensator (SSSC) to improve the damping of inter-area oscillations by considering the time delays of remote control signals. For this aim, the control gain value of the WADC, shifting of the critical modes to a desirable area, and the maximum time delay margins are simultaneously considered as an objective function. Moreover, to design a WADC as minimum-phase structure, the suitable constraints have been determined and added to the objective function as penalty factors. A grey wolf optimization (GWO) algorithm is utilized to solve the optimization problem. Robustness of the designed SSSC-based WADC in amplitude and phase control channels against the time delay has been compared and then the best control channels to damp inter-area oscillations has been selected. Based on simulations and statistical results conducted on 4 and 50 machine multimachine power systems, it is evident that the proposed method based on the GWO outperforms other techniques in achieving the optimal design of a SSSC-based WADC for damping inter-area oscillations.

关键词： SSSC Wide area damper Time delay Inter -area oscillation grey wolf optimization algorithm Control channel

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Seasonal waste, geotherm, nuclear, wood net power generations forecasting using a novel hybrid grey model with seasonally buffered and time-varying effect

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APPLIED ENERGY 2024年 368卷

作者： Li, Xuemei Shi, Yansong Zhao, Yufeng Wu, Yajie Zhou, Shiwei Ocean Univ China Sch Econ Qingdao 266100 Peoples R China Ocean Univ China Inst Marine Dev Qingdao 266100 Peoples R China Ocean Univ China Sch Management Qingdao 266100 Peoples R China Ocean Univ China Coll Engn Qingdao 266100 Peoples R China

Seasonal volatility data is often disturbed by uncertain external shocks, making accurate forecasting particularly strenuous. This paper proposes a progressive adaptive prediction framework of data preprocessing, feature recognition, and seasonal prediction, namely SAWBO-TNGBM (1,1) model. Specifically, the seasonal full information variable weight weakening buffering operator is employed to effectively smooth the nonlinear fluctuation data. Furthermore, the grey Bernoulli model is extended by considering the time-varying effect, and grey wolf optimization algorithm improves the overall prediction efficiency. Necessarily, the Convertibility, Unbiasedness, and Recursiveness are fully derived and proven, which undoubtedly improves the reliability and the ability to capture seasonal information. Empirically, from a data-driven perspective, US seasonal clean energy net generations with diverse fluctuating characteristics are utilized to validate the predicted performance, including quarterly series (waste, geotherm) and monthly series (nuclear, wood). Results obtained from comprehensive experimental comparative analyses show that the fitting ability of the SAWBO-TNGBM (1,1) model exceeds that of other models, demonstrating its flexibility, universality, and high precision. Lastly, innovative robustness testing and extended analysis ensure that the novel model provides an effective tool for seasonal forecasting in clean energy generation.

关键词： Seasonal clean energy generation Seasonal weakening buffer operator grey prediction model grey wolf optimization algorithm Time-varying effect

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optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

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IEEE ACCESS 2024年 12卷 74169-74184页

作者： Zhang, Na Wang, Xiaofeng Henan Finance Univ Sch Comp & Artificial Intelligence Zhengzhou 450046 Peoples R China SEGi Univ Grad Sch Business GSB Kota Damansara 47810 Petaling Jaya Malaysia

Classification algorithms are one of the important research topics in the artificial intelligence, widely applied in various scientific and engineering fields. Extreme learning machine is a single hidden layer feed-forward neural network algorithm. Compared with traditional neural network models, the training speed and the generalization ability are also better. In terms of methodology, this study first innovatively improves the traditional grey wolf optimization (GWO) algorithm to enhance its convergence and search ability. Specific improvement measures include implementing the reverse learning strategy to reduce the initial dependence of the algorithm on population distribution, and adding exploration perception strategy to enhance its global search ability by calculating heuristic factors, so as to identify the global optimal solution more effectively. The results showed that the improved W-DH-ELM model had excellent performance on multiple standard data sets. In particular, the average accuracy was more than 90%, which was significantly higher than other benchmark classification models. In terms of operation efficiency, the running time of the new model on different data sets was significantly reduced, accounting for less than 25%, and the lowest running time was only 4.89%. These experimental results verify the effectiveness of the introduced intelligent optimization algorithm in improving the performance of traditional ELM model without changing the original model structure. The improved W-DH-ELM model not only maintains the fast training performance of ELM, but also has higher accuracy and stability, which shows its superiority in dealing with complex classification tasks. In summary, the weighted two-hidden layer extreme learning machine optimized by the improved GWO proposed in this study has significant advantages in classification problems, providing a new perspective for future machine learning applications and research.

关键词： optimization Classification algorithms Convergence Predictive models Heuristic algorithms Prediction algorithms Feature extraction Learning systems Reverse logistics Two-hidden layer extreme learning machine grey wolf optimization algorithm classification model reverse learning perception strategy

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A hybrid coal prediction model based on grey Markov optimized by GWO - A case study of Hebei province in China

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 235卷

作者： Xu, Yan Lin, Tong Du, Pei Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Dongbei Univ Finance & Econ Qingdao Financial Res Inst Qingdao 266100 Peoples R China Jiangnan Univ Inst Food Safety Risk Management Wuxi 214122 Peoples R China

Accurate prediction of coal consumption is crucial to the structural adjustment and high-quality development of the coal industry, and provides an effective basis for the government to formulate energy strategies. To enhance the prediction accuracy, a hybrid prediction model combining grey wolf optimization algorithm and grey Markov model (GWO_Markov_DNGM) is proposed in this study, which introduces the idea of Markov interval division in order to make up the defect of obtaining interval by experience in the past. In this research, coal consumption in Hebei Province from 2001 to 2020 is used as an example to verify the accuracy of the model and the results show that the proposed model has higher accuracy than the comparison models, including ARIMA, GM (1, 1), DGM (1, 1), DNGM (1, 1) and the grey Markov model with equidistant division of state interval. In addition, this study also discusses the influence of numbers of state intervals on the prediction accuracy of the proposed model, which further illustrates the high prediction accuracy of the proposed hybrid model. Finally, the proposed model is employed to predict the coal consumption of Hebei Province under three scenarios from 2021 to 2025, and several suggestions are put forward.

关键词： Coal consumption prediction grey prediction model grey wolf optimization algorithm Markov chain model

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Construction of source load uncertainty economic dispatch model based on distributed robust opportunity constraints

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Energy Informatics 2025年第1期8卷 1-14页

作者： Li, Jinjian Information Center Guangxi Zhuang Autonomous Region Company of China National Tobacco Corporation Nanning530000 China

With the increasing demand for electricity, the power system is facing enormous challenges. To ensure the equilibrium between supply and demand in the electricity market and the safety and stability of the power grid, a source load uncertainty economic dispatch model based on distributed robust opportunity constraints is proposed to cope with the uncertainty of sustainable energy resources such as wind power and photovoltaics. By introducing an improved Elman network and grey wolf optimization algorithm, high-precision prediction of short-term loads is achieved, providing data support for scheduling models. The experiment outcomes indicate that the prediction model grounded on the improved Elman network and grey wolf optimization algorithm performs the best in scheduling performance on both the training and testing sets, with the lowest cost, the highest utilization rates of wind and solar power, and the lowest probability of constraint default. In addition, the economic dispatch model proposed by the research has significant advantages in reducing total dispatch costs, improving wind and photovoltaic utilization rates, and constraining default probability control. In typical load scenarios, the total scheduling cost of the model is $1,308,469, with wind and photovoltaic utilization rates reaching 90.5% and 86.1% respectively, and a default probability of only 0.9%. The research results indicate that the model exhibits superiority in real-time response time, especially suitable for scenarios with high load fluctuations. The research provides important theoretical basis and application value for the economic dispatch of power systems. © The Author(s) 2025.

关键词： Distributed robust opportunity constraint Elman network grey wolf optimization algorithm Power system Source load uncertainty

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An improved grey wolf algorithm and its localization research in complex indoor environments

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Li, Bing Hao, Yanxin Cui, Yiyang Chai, Xingshao Zhou, Jingmei Liu, Chungang Hebei Normal Univ Coll Combust Engn Shijiazhuang 050024 Peoples R China Hebei Prov Key Lab Informat Fus & Intelligent Cont Shijiazhuang 050024 Peoples R China Hebei Prov Innovat Ctr Wireless Sensor Network Dat Shijiazhuang 050024 Peoples R China

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse

关键词： Ultra-wideband technology 3D indoor localization Swarm algorithm grey wolf optimization algorithm Localization accuracy

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Performance optimization of high-rise residential buildings in cold regions considering energy consumption

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DISCOVER APPLIED SCIENCES 2025年第2期7卷 1-14页

作者： Song, Liwei Jilin Technol Coll Elect Informat Acad Affairs Off Jilin 132021 Peoples R China

With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.

关键词： Energy consumption High-rise residential buildings grey wolf optimization algorithm Multi-objective optimization Enclosure structure

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