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optimization of PID controller parameters using a hybrid PSO algorithm

INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第10期12卷 3617-3627页

作者： Zhang, Xia Yang, Yue Xiangnan Univ Coll Comp & Artificial Intelligence Chenzhou 423000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

PID controller parameter optimization is crucial for improving system stability, reliability, and responsiveness. To improve the efficiency of a PID controller, a hybrid algorithm, HPSO, based on particle swarm optimization algorithm (PSO) and simulated annealing (SA) algorithms is proposed. The HPSO algorithm is used to optimize the control parameters. Adaptive weights and dynamic learning factors are introduced to improve the global optimization ability of the PSO algorithm. The simulated annealing (SA) mechanism is incorporated into the particle swarm optimization (PSO) algorithm. The Metropolis criterion and cooling mechanism are used to guide the population to accept inferior solutions with dynamic jump probabilities, so that the algorithm can escape local optima and effectively improve its global search ability. The experimental results show that compared with the SA algorithm, basic particle swarm optimization algorithm and inertial weight coefficient particle swarm optimization algorithm, the HPSO algorithm has fast convergence speed and strong global search ability. Then, the population particles are defined using the coefficients to implement a unique fitness function, which is consistent with the control objectives of achieving fast system response while minimizing overshoot. Finally, taking a typical second-order temperature control delay system and CNC machine feed servo system as the control objects, the HPSO algorithm is compared with genetic algorithm and seeker optimization algorithm. The simulation results show that the optimized PID parameters obtained from the HPSO algorithm has low overshoot, better steady-state, and dynamic response.

关键词： particle swarm optimization algorithm Simulated annealing algorithm PID controller

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Estimation of Weibull Probability Distribution Parameters with optimization algorithms and Foca Wind Data Application

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2024年第3期37卷 1236-1254页

作者： Kose, Bayram Isikli, Ibrahim Sagbas, Mehmet Izmir Bakircay Univ Elect & Elect Engn Dept TR-35665 Izmir Turkiye

In this study, the scale and shape parameters of the Weibull probability distribution function (***) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foca region of Izmir in Turkey. The values of Weibull parameters obtained using particle swarm optimization (PSO), Sine Cosine algorithm (SCA), Social Group optimization (SGO), and Bat algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foca were used. As a result of Foca speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and chi<^>2 criteria are evaluated together;it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

关键词： Weibull distribution parameters particle swarm optimization algorithm Social group optimization algorithm Sine cosine optimization algorithm Bat algorithm

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Fundamental Relation Between Phased Array Radiation and Scattering Fields

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2023年第9期71卷 3797-3809页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, the scattering mechanism of phased array antennas is systematically analyzed from the perspective of the scattering matrix representation. The scattering problem of an N-element phased array antenna is equivalent to an (N + 1)-port microwave network described by an (N + 1) x (N + 1) scattering matrix. The physical meanings of the coefficients in the matrix are analyzed, which imply different aspects of antenna properties like impedance matching and coupling, receiving, reradiation, and structural mode scattering. The coefficients associated with antenna receiving and reradiation are further studied to reveal the fundamental relation between phased array scattering and radiation fields. Eventually, the formulas to compute phased array scattering fields by radiation fields are deduced. Utilizing the formulas, phased array scattering under different element loads can be predicted precisely and rapidly by the precomputed data of an original phased array rather than repeating time-consuming full-wave computations. Furthermore, based on the fast scattering prediction approach, an efficient modified discrete particle swarm optimization algorithm is proposed to optimize the phased array element loads, and impressive improvement is made against the reported phased array scattering reduction approaches. The proposed scattering prediction and reduction approaches are well validated numerically and experimentally.

关键词： Scattering Phased arrays Microwave antenna arrays Loaded antennas Electromagnetic scattering Antennas Impedance particle swarm optimization algorithm phased array radiation and scattering scattering matrix representation scattering prediction scattering reduction

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Performance analysis of hybrid geometric-probabilistic shaped 8/12 quadrature amplitude modulation for coherent optical communication system

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OPTICAL ENGINEERING 2023年第6期62卷

作者： Xu, Junping Ma, Jie Liu, Jianfei Lu, Jia Zeng, Xiangye Luo, Mingming Hebei Univ Technol Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin Peoples R China

. A hybrid shaping (HS) scheme based on geometric shaping (GS) and probabilistic shaping (PS) in a coherent optical communication system is proposed. A particle swarm optimization algorithm and Maxwell-Boltzmann distribution are employed to sequentially implement GS and PS. The results demonstrate that hybrid shaped 8/12-ary quadrature amplitude modulation (HS-8/12QAM) is superior to regular-8/12QAM (R-8/12QAM) in terms of reducing the bit error rate (BER) and increasing the generalized mutual information (GMI). HS-8QAM achieves a 2 dB optical signal-to-noise ratio (OSNR) gain and 0.45bits/symbol GMI gain compared with R-8QAM. Meanwhile, HS-12QAM achieves 1.9 dB OSNR gain and 0.68 bits/symbol GMI gain compared with R-12QAM. In addition, HS-8/12QAM is better than R-8/12QAM in terms of transmission distance and data rate.

关键词： coherent optical communication particle swarm optimization algorithm hybrid shaping generalized mutual information

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Deep optimization design of 2D repetitive control systems with saturating actuators: An adaptive multi-population PSO algorithm

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ISA TRANSACTIONS 2023年 140卷 342-353页

作者： Wang, Yibing Zhang, Manli Lu, Chengda Tian, Shengnan She, Jinhua Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Tokyo Univ Technol Sch Engn Hachioji 1920982 Japan

This paper presents an optimization design method for a two-dimensional (2D) modified repetitive control system (MRCS) with an anti-windup compensator. Using lifting technology, a 2D hybrid model of the MRCS considering actuator saturation is established to describe the control and learning of the repetitive control. A linear-matrix-inequality (LMI)-based sufficient condition is derived to ensure the stability of the MRCS. Two tuning parameters, the selection of which is critical to the system design, are used in the LMI to adjust the control and learning, and hence the reference-tracking performance. A new cost function, developed through time domain analysis, directly evaluates the control performance of the system without calculating control errors, thus reducing the optimization time. Based on this cost function, an adaptive multi-population particle swarm optimization algorithm is presented to select an optimal pair of tuning parameters in which multiple populations cooperatively search in non-intersecting search intervals. An anti-windup term is added between the low-pass filter and the time delay in the modified repetitive controller to mitigate the undesirable effect of actuator saturation on system performance and stability. Simulations and experiments on the speed control of a rotation control system demonstrate the validity of the approach. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： 2D modified repetitive control particle swarm optimization algorithm Adaptive multi-population framework Anti-windup compensator

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Modelling and forecasting non-renewable energy consumption and carbon dioxide emissions in China using a PSO algorithm-based fractional non-linear grey Bernoulli model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第26期30卷 69651-69665页

作者： Yang, Jie Wu, Zhiqiang Shanghai Elect Power Univ Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO2) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO2 emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO2 emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO2 emissions is modeled. On this basis, China's future CO2 emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO2 emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO2 emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals.

关键词： Non-renewable energy consumption Carbon dioxide emissions Fractional non-linear grey Bernoulli model particle swarm optimization algorithm

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Research on fault location algorithm of TPSS based on PSOA

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1213-e1213页

作者： Jia, Yunqian Shanghai Inst Technol Sch Railway Transport Shanghai Peoples R China

It is extremely important to research traction power supply system (TPSS) protection technology in order to ensure the safe operation of urban rail transit. A TPSS includes rails, return cables, rail potential limiting devices, one-way conducting devices, drainage cabinets, ballast beds, and tunnel structural reinforcements. In urban rail transit, on the basis of the dynamic characteristics of the TPSS, a fault location algorithm based on particle swarm optimization algorithm (PSOA) is developed. An evaluation of multi-point monitoring data is proposed based on fuzzy processing of the average value of polarization potential forward deviation and multi-attribute decision-making. Monitoring points and standard comparison threshold values are determined by the distribution law of stray currents. In conjunction with the actual project, the model is trained using field measured data. Based on the results, TPSSOA is able to achieve optimal discharge current control, reduce network losses and improve power quality. Moreover, the reconstruction results demonstrate the high usability of the proposed method, which will provide guidance to design the TPSS in the future.

关键词： Traction power supply Machine learning particle swarm optimization algorithm Fault location

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Low-Scattering-Sidelobe Phased Array Antenna With Improved Radiation Performance

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第10期71卷 7837-7847页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Jiang, Ming Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, the formulas to compute phased array scattering fields under oblique incidences are derived as the theoretical basis of scattering sidelobe level (SLL) reduction. Meanwhile, in order to improve antenna radiation performance, the way to compute and evaluate array impedance matching performance is proposed based on an equivalent network model. The accuracy and efficiency of the formulas applied to predict array scattering fields and reflection coefficients are well validated by numerical study. Furthermore, a microstrip phased array antenna with a tapered array shape and optimized element feed networks is designed. A modified particle swarm optimization algorithm is proposed to search the solution of feed networks to reduce monostatic scattering SLL and improve antenna impedance matching simultaneously, where the proposed fast prediction approaches are employed. The optimized phased array antenna features scattering SLL reduction by 8.8 dB in the concerned angular range of 10 degrees-30 degrees and in the operating band of 5.5-7.5 GHz against its conventional counterpart, with improved impedance matching and slightly higher gains. Eventually, the numerical results are well validated in experiments.

关键词： Equivalent network model improved radiation performance particle swarm optimization algorithm phased array scattering prediction scattering sidelobe level (SLL) reduction

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A novel conformable fractional-order accumulation grey model and its applications in forecasting energy consumption of China

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Chen, Yuzhen Gong, Wenhao Li, Suzhen Guo, Shuangbing Henan Inst Sci & Technol Sch Math Sci Xinxiang 453003 Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

Accurate forecasting of energy consumption demand is crucial to optimize resources and achieve sustainable development goals. However, the energy system is affected by many factors, which are complex and highly uncertain. Therefore, a novel grey model (IBCFGMP (1,1,N)) is proposed, integrating multiple optimization techniques such as background value optimization, initial condition optimization, fractional-order accumulation optimization, and grey action quantity optimization. First, this paper deduces the time response function of the optimization model. The relevant parameters of the model can be found using the particle swarm optimization algorithm. Then, the properties of the model are studied, and it is found that the optimized model have stronger universality and stability. Finally, the model is applied to predict and analyze the energy consumption of China. The prediction results indicate that China's consumption of hydroelectricity, nuclear energy, and coal will be 12.693 exajoules, 5.550 exajoules, and 98.850 exajoules in 2026, respectively. The research results will provide a scientific basis for rationally optimizing resource allocation and realizing the sustainable development of clean energy.

关键词： Grey prediction model Conformable fractional-order particle swarm optimization algorithm Clean energy IBCFGMP (1,1,N) model

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Displacement of mining vibrating screen obtained from acceleration based on improved S-G filter

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Xiao, Linjing Lu, Hao Yan, Fangping Shandong Univ Sci & Technol Dept Elect & Informat Jinan 250031 Shandong Peoples R China Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky-Golay (S-G) filter is proposed. The particle swarm optimization (PSO) algorithm is used to optimize the window length of the S-G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

关键词： Vibration displacement Acceleration signals Improved Savitzky-Golay filter particle swarm optimization algorithm Frequency-domain integration

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