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optimization control of spacecraft proximation based on r-domination adaptive bare-bones particle swarm optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Zhu, Zhihao Guo, Yu Gao, Zhi Yancheng Inst Technol Sch Elect Engn Yancheng Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China Yancheng Inst Technol Sch Informat Engn Yancheng Peoples R China

This paper proposes a novel finite-time (FT) optimization control approach of spacecraft proximation based on a new r-domination adaptive bare-bones multi-objective particle swarm optimization scheme (r-ABBMOPSO). Specifically, a new adaptive particle update strategy is developed for bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) to enhance the robustness of the search. To make the search toward the desired point, r-ABBMOPSO applies r-domination to replace Pareto-domination. In addition, a new adaptive mutation algorithm is designed to strong the population search diversity. By virtue of r-ABBMOPSO to obtain the optimal control parameters, a FT six degrees of freedom (6-DOF) proximation controller with the adaptive update laws of the unknown parameters is proposed to regulate chaser spacecraft approach to target spacecraft. Finally, numerical comparison examples illustrate the performance of the proposed optimization controller.

关键词： Finite-time optimization control Adaptive particle update Adaptive mutation Six degrees of freedom

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Speed optimization control of a Permanent Magnet Synchronous Motor Based on TD3

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ENERGIES 2025年第4期18卷 901-901页

作者： Hu, Zuolei Zhang, Yingjie Li, Ming Liao, Yuhua Hunan Univ Coll Informat Sci & Elect Engn Changsha 410000 Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Univ Manchester Dept Mech Aerosp & Civil Engn Manchester M13 9PL England

Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation and electric vehicles due to their high efficiency and excellent dynamic performance. However, controlling PMSMs presents challenges such as parameter variations and system nonlinearities. This paper proposes a twin delayed deep deterministic policy gradient (TD3)-based energy-saving optimization control method for PMSM drive systems. The TD3 algorithm uses double networks, target policy smoothing regularization, and delayed actor network updates to improve training stability and accuracy. Simulation experiments under two operating conditions show that the TD3 algorithm outperforms traditional proportional-integral (PI) controllers and linear active disturbance rejection control (LADRC) controllers in terms of reference trajectory tracking, q-axis current regulation, and speed tracking error minimization. The results demonstrate the TD3 algorithm's effectiveness in enhancing motor efficiency and system robustness, offering a novel approach to PMSM drive system control through deep reinforcement learning.

关键词： PMSM TD3 optimization control energy-saving

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Data-Driven Distributed Diagnosis and optimization control for Cascaded Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第5期54卷 3001-3010页

作者： Wang, Hao Luo, Hao Jiang, Yuchen Kaynak, Okyay Harbin Inst Technol Dept Control & Simulat Ctr Harbin 150001 Peoples R China Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkiye

Due to limitations in large-area communication and computation, it can be challenging to apply centralized diagnosis and optimization control design approaches to cascaded systems. This work proposes a distributed diagnosis and optimization control approach, which is realized using data-driven techniques. Specifically, an adaptive observer-based subdiagnosis system design approach is proposed for cascaded systems using only the local input/output (I/O) data and the state estimations of adjacent subsystems. The state estimations from neighboring subsystems are treated as known inputs in the local subsystem. In the centralized design approach, the residual signals generated by all subsystem observers need to be sent to the central computing node to reconstruct controller parameters. The learning process of the local optimization controller only needs to be driven by the residual signals from local and adjacent subsystems, avoiding centralized calculation and reducing the computational burden of the central node. The learning process of the locally optimal controller only needs to be driven by residual signals from the local and neighboring subsystems. In the end, the simulation results verify the effectiveness of the proposed distributed approach.

关键词： Cascaded systems data-driven distributed diagnosis optimization control

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Sensitivity analysis of aero-engine performance optimization control system and its hardware test verification

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2025年第2期42卷 433-446页

作者： Fang, Juan Zhang, Nan Liu, Weimin Wuxi Inst Technol Wuxi 214121 Peoples R China AECC Aeroengine Control Syst Inst Wuxi 214063 Peoples R China

The performance seeking control (PSC) method is aimed at improving the performance of aircraft engine without changing hardware devices. However, the complexity of the system makes it difficult to achieve its application in engineering. Thus, the key issues in PSC of turbofan engine are studied based on global sensitivity analysis, and the improvements are made to the optimization system according to the analysis. Firstly, the principle of PSC is analyzed based on traditional frameworks. And an improved linear programming method based on task scheduling is proposed to reduce the time consumption within a single closed-loop simulation. The impact of the estimation model accuracy on optimization results is studied adopting a sensitivity analysis method based on Monte Carlo sampling to address the optimization lose efficacy. Finally, the hardware-in-the-loop simulation is carried out utilizing model-based design method in a certain type of turbofan engine electronic controller.

关键词： optimization control areo-engine control real-time performance onboard algorithm

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Dynamic of the air pollution pathogenic model and optimization control

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INTERNATIONAL JOURNAL OF BIOMATHEMATICS 2025年第0期

作者： Hippolyte, Bessane Son-Ongaya Merveil Qi, Longxing Anhui Univ Sch Math Sci Jiulong Rd Hefei 230601 Peoples R China

Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere. In this paper, the ability of air pollutants to affect the occurrence of infectious respiratory diseases in a population is studied by establishing an SISP respiratory disease model. It is found that the inhalation rate of pollutants, based on the daily emission level of pollutants, has a huge impact on the development of respiratory diseases. It is also found that the existence of multiple endemic equilibria, local stability and global stability of the equilibria, all depend on a certain quantity of pollutant produced in the environment. Moreover, the sensitivity analysis shows that the daily emission of pollutants and the inhalation rate of pollutants have significant positive correlation on the model while the clearance rate of pollutants have strong negative correlations with the model. Based on these results, an control strategy is produced in order to reduce the impact of air pollution on the occurrence of respiratory diseases in our environment and it goes as follows: the first step is to reduce the conversion rate of susceptible people to infective by reducing the inhalation rate of pollutant through raising public awareness on how to protect oneself from air pollutants in order to prevent respiratory infections;the second step is to reduce the daily emission of pollutants from all sources of emissions and try to keep it as low as possible;and the last step is to improve the clearance rate of pollutants in the environment.

关键词： Respiratory disease air pollution daily emission level inhalation rate optimization control

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An Improved Dynamic optimization control System for the Drilling Rate of Penetration (ROP) and Its Industrial Application

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第6期70卷 6201-6208页

作者： Gan, Chao Cao, Wei-Hua Wang, Lu-Zhao Liu, Kang-Zhi Wu, Min China Univ Geosci Sch Automation Wuhan 430074 Peoples R China Key Lab Adv Control & Intelligent Automation Compl Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China No 3 Explorat Inst Geol & Mineral Resources Shando Yantai 264000 Peoples R China Chiba Univ Dept Elect & Elec tron Engn Chiba 2638522 Japan

optimization control of the rate of penetration (ROP) is crucial in the drilling process, due to its vital role in improving the drilling efficiency and safety. In this article, an improved dynamic optimization control system for ROP is proposed and successfully applied to a drilling site. First, a three layers (i.e., intelligent optimization layer, basic automation layer, and process monitoring layer) system framework is proposed according to the drilling characteristics. After that, "if-then " strategy is used as the method to identify the rotary drilling condition from four different working conditions. A moving window strategy is selected to establish the dynamic ROP model that can better adapt to environmental changes. Moreover, a Jaya algorithm, a new meta-heuristic optimization method, is introduced to solve the dynamic ROP optimization issue. In the simulations, the optimization performance of the proposed system is better than the moving window-extreme learning machine-hybrid bat algorithm with more stable (fewer switch times) drilling operational parameters. Finally, in an industrial application to a drilling process in Dandong area, Northeast China, the ROP is improved by 20.79% compared with manual operation. Both simulation and industrial application results indicate that the proposed system has significantly improved the drilling efficiency and safety.

关键词： Drilling optimization optimization methods Heuristic algorithms control systems Automation Employee welfare Drilling efficiency drilling process dynamic optimization optimization control rate of penetration (ROP)

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optimization control of hydrogen engine ignition system based on ACO-BP

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第78期46卷 38903-38912页

作者： Dang, Jinjin Wang, Lijun Luoyang Polytech Sch Automobile & Rail Transit Luoyang 471400 Peoples R China North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou 450045 Peoples R China

With the development of new energy, hydrogen fuel engines have become a research boom in the automotive field. But there are abnormal problems such as backfire and pre-ignition during the combustion of hydrogen engines. This paper is based on the Ant Colony optimization-Back Propagation (ACO-BP) algorithm to study the influence of different speed and load conditions on the ignition advance angle, so as to optimize the control of the hydrogen engine. The experimental system is established on a hydrogen engine converted from a 492Q gasoline engine. The prediction of the optimal ignition advance angle was obtained through experiments, and the optimal ignition MAP diagram of the hydrogen engine is constructed. The optimal ignition advance angle under different working conditions can effectively avoid the occurrence of hydrogen engine pre-ignition. The accuracy reaches 0.0018209 when the training reaches 14 times, the fitness between the actual value and the predicted value of ACO-BP training is 0.99921, the verification accuracy reaches 0.99913, and the test accuracy reaches 0.99932. Compared with the three optimization methods, the convergence speed and error accuracy of ACO-BP are significantly better than the BP neural networks and Genetic Algorithm-Back Propagation (GA-BP). This method realized the model of the nonlinear mapping model from hydrogen engine speed and load to optimal ignition advance angle, which is of great significance for solving the problem of abnormal combustion in hydrogen engine. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： optimization control Hydrogen engine ACO-BP Abnormal combustion

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Demand Response and optimization control for Fresh Air of Building Air Condition Systems 24

Demand Response and Optimization Control for Fresh Air of Bu...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Zhang, Lei Luo, Ziyi Zhang, Ji Xiao, Weidong Jiang, Chunbing Liu, Yao North China Branch State Grid Corp China Elect Power Dispatching & Control Ctr Beijing Peoples R China Tsinghua Univ Dept Bldg Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798400716638

The increasing electricity demand and the developing renewable energy sources pose challenges to the stable operation of new power systems. Demand response has become a crucial measure to ensure the balance between electricity supply and demand. Heating, ventilation, and air conditioning (HVAC) systems are essential flexible response resources in buildings, with a high proportion of fresh air load energy consumption. However, it remains uncertain whether the fresh air systems can participate in the response and regulation of the power system and to what extent they can provide response potential. This article takes the fresh air system of office buildings as an example, and establishes direct and indirect response potential evaluation methods with the goal of minimizing unit hourly energy consumption and daily operating costs, respectively. The results indicate that the fresh air system has a fast regulation rate and is an excellent short-term response resource. When the personnel density is low, the response potential is large, and the energy consumption can be reduced by 20% to 100% per hour. The all-day response strategy based on time of use electricity prices can reduce operating costs by 14%. Moreover, this article further proposes a dual layer optimization control strategy that comprehensively considers short-term load shaving and long-term cost saving. It ensures maximum reduction of peak load while reducing overall operating costs, and has high application value for improving the contradiction between power supply and demand.

关键词： intelligent building technology power demand response optimization control fresh air load carbon dioxide air conditioning system

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Network-Side Power optimization control Considering Photovoltaic and Energy Storage Connection to RPC-Type Traction Power Supply System 6th

Network-Side Power Optimization Control Considering Photovol...

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6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT)

作者： Mu, Xiuqing Chen, Xiaoqiang Wang, Ying Yang, Qiaoli Zhang, Yunpeng Lanzhou Jiaotong Univ Lanzhou 730070 Peoples R China

ISBN: (纸本)9789819993093;9789819993079;9789819993062

Multi-source power supply for electrification of the railroad is essential for having a sustainable energy supply to drive critical EMU loads. Firstly, the working theory of railway power conditioner (RPC) is analyzed, and the "optical-storage-locomotive-network" model is built. Secondly, the specific working mode is proposed. Thirdly, the energy flow under various modes of the RPC converter are analyzed, and a cooperative control scheme for compensating the output power of the traction network by the photovoltaic storage system through the railway power conditioner is presented. Finally, the cooperative control scheme is verified and analyzed by Starsim HIL real-time test platform.

关键词： Railway power conditioner (RPC) Traction network output power Photovoltaic and energy storage optimization control Test verification

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Intelligent optimization control for Air Starting Systems Based on Deep Reinforcement Learning 35

Intelligent Optimization Control for Air Starting Systems Ba...

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35th Chinese control and Decision Conference (CCDC)

作者： Peng, Jin Li, Xin Guo, Zhongyu Yang, Wenda Xu, Hongzhang Qi, Yiwen AECC Sichuan Gas Turbine Estab Whole Engine Lab Mianyang 621703 Sichuan Peoples R China Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

ISBN: (纸本)9798350334722

In this paper, an intelligent optimization control method based on deep reinforcement learning is proposed for air starting systems of altitude test facility. A deep Q-network (DQN) controller with self-learning capability is employed to effectively improve the dynamic and steady-state performance of the air starting system under pressure and flow disturbances. Key design methods including state space selection, action output and reward function design are studied and given. Simulation results show that compared with PID control, the designed controller can realize no overshoot adjustment of the intake pressure under constant or variable operating conditions, and the adjustment time is shorter. The results verify the rapidity, stability and robustness of the proposed intelligent optimization control method.

关键词： Altitude test facility Air starting systems Deep reinforcement learning optimization control

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