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学者姓名：林忠麟
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Abstract ：
Unmanned Aerial Vehicles (UAVs) are playing an increasingly critical role in military surveillance missions. However, due to safety and economic issues, it is necessary to validate the UAV performance and algorithms in a semi-physical simulation environment before the real flights. In this study, the mechanical structure and control system of the semi-physical simulation system is developed for the UAVs. In order to better simulate the attitude of the UAV during a flight, the flight attitude simulator is designed. Also, a sand table of the mountainous scenery from real inspection tasks is developed to work with the simulation system. Path planning algorithms are embedded in the platform, and improvements and evaluations of traditional algorithms are carried out. Then, an improved adaptive particle swarm optimization (IAPSO) algorithm is proposed to improve the accuracy of the path of UAVs. An energy-consumption prediction model is established for the platform. By combining the IAPSO algorithm with the energy-consumption prediction model, the best path with low energy consumption can be obtained. Finally, the effectiveness of the simulation system is verified by comparison experiments of the mathematical simulation, semi-physical simulation, and real flight. © 2024 Elsevier Ltd
Keyword ：
Flight simulators Flight simulators Military vehicles Military vehicles Motion planning Motion planning Particle swarm optimization (PSO) Particle swarm optimization (PSO) Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Lin, Zhonglin , Li, Yufeng , Qian, Qiang et al. Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery [J]. | Computers and Industrial Engineering , 2024 , 197 . |
| MLA | Lin, Zhonglin et al. "Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery" . | Computers and Industrial Engineering 197 (2024) . |
| APA | Lin, Zhonglin , Li, Yufeng , Qian, Qiang , Huang, Feng , Zhang, Xinglong , Zhang, Tianhong et al. Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery . | Computers and Industrial Engineering , 2024 , 197 . |
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This study proposes an active surge control method based on deep reinforcement learning to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an aeroengine. Initially, the study establishes the compressor dynamic model with uncertainties, disturbances, and Close-Coupled Valve (CCV) actuator delay. Building upon this foundation, a Partially Observable Markov Decision Process (POMDP) is defined to facilitate active surge control. To address the issue of unobservability, a nonlinear state observer is designed using a finite-time high-order sliding mode. Furthermore, an Improved Soft Actor-Critic (ISAC) algorithm is developed, incorporating prioritized experience replay and adaptive temperature parameter techniques, to strike a balance between exploration and convergence during training. In addition, reasonable observation variables, error-segmented reward functions, and random initialization of model parameters are employed to enhance the robustness and generalization capability. Finally, to assess the effectiveness of the proposed method, numerical simulations are conducted, and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control (SOSMC) method. The simulation results demonstrate that the deep reinforcement learning based controller outperforms other methods in both tracking accuracy and robustness. Consequently, the proposed active surge controller can effectively ensure stable operation of compressors in the high-pressure-ratio and high-efficiency region. © 2024
Keyword ：
Active surge control Active surge control Aeroengine surge Aeroengine surge Deep reinforcement learning Deep reinforcement learning Moore-Greitzer model Moore-Greitzer model Nonlinear observer Nonlinear observer Soft actor-critic Soft actor-critic
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| GB/T 7714 | ZHANG, X. , LIN, Z. , JI, R. et al. Deep reinforcement learning based active surge control for aeroengine compressors [J]. | Chinese Journal of Aeronautics , 2024 , 37 (7) : 418-438 . |
| MLA | ZHANG, X. et al. "Deep reinforcement learning based active surge control for aeroengine compressors" . | Chinese Journal of Aeronautics 37 . 7 (2024) : 418-438 . |
| APA | ZHANG, X. , LIN, Z. , JI, R. , ZHANG, T. . Deep reinforcement learning based active surge control for aeroengine compressors . | Chinese Journal of Aeronautics , 2024 , 37 (7) , 418-438 . |
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Abstract ：
The energy consumption of pneumatic systems is occupying an increasingly considerable proportion in the industrial systems. However, due to the response characteristics of actuators, the pneumatic control system generally has a low energy utilization efficiency. How to improve the response accuracy of the pneumatic system while reducing energy consumption remains a key problem to be solved. In this paper, a three-voltage acceleration waveform and its generation method are proposed, and the acceleration circuit is designed. A multi-mode acceleration switching strategy and backstepping sliding mode controller (BSMC) are applied. The test results show that compared to the traditional methods, BSMC respectively saves 26.27% of the air consumption, as well as 32.35% of the valve group power consumption. It also achieves the lowest root-mean-square error (RMSE), of 4.8421 kPa. All the experiments prove that the controller proposed can effectively improve the energy utilization efficiency while maintaining high tracking precision.
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| GB/T 7714 | Lin, Zhonglin , Wang, Haitao , Zhang, Xinglong et al. Enhanced energy conservation and response accuracy of a pneumatic control system [J]. | ISCIENCE , 2024 , 27 (9) . |
| MLA | Lin, Zhonglin et al. "Enhanced energy conservation and response accuracy of a pneumatic control system" . | ISCIENCE 27 . 9 (2024) . |
| APA | Lin, Zhonglin , Wang, Haitao , Zhang, Xinglong , Liu, Wenchao , Gan, Jinyu , Huang, Feng et al. Enhanced energy conservation and response accuracy of a pneumatic control system . | ISCIENCE , 2024 , 27 (9) . |
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In the aero-engine hardware-in-the-loop simulation test, the compressor outlet pressure simulation is an important part. In order to simulate the sudden change of airflow in the process of compressor destabilization, a high-precision pressure simulation system is designed, through which a real pressure signal is generated to provide an accurate pressure excitation for the full authority digital engine control. A multi-mode acceleration switching strategy is proposed according to the characteristics of the switching valve, and the acceleration drive waveform and generation method are designed to combine with the field-programmable gate array module to accurately output the acceleration waveform and improve the opening and closing speeds of switching valve. A seven-mode switching method is designed based on the traditional three-mode and five-mode switching methods. The backstepping sliding mode controller is combined with the seven-mode switching method to improve the control accuracy, and the stability of control system is proven. The test results show that the maximum average overshoot in the steady-state pressure test is 1. 25%, and the maximum average steady-state error is 0. 016 MPa. The average errors in the random step response and sinusoidal tracking tests are less than 0. 02 MPa. The proposed pressure simulation method can meet the requirements for high-accuracy pressure simulation of aero-engine hardware-in-the-loop simulation tests and achieve the accurate pressure simulation at a low cost. © 2024 China Ordnance Industry Corporation. All rights reserved.
Keyword ：
Backstepping Backstepping Braking Braking Engines Engines Field programmable gate arrays (FPGA) Field programmable gate arrays (FPGA) Hardware-in-the-loop simulation Hardware-in-the-loop simulation Random errors Random errors Sliding mode control Sliding mode control Synthetic apertures Synthetic apertures
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| GB/T 7714 | Lin, Zhonglin , Wang, Haitao , Liu, Wenchao et al. Aero-engine Compressor Pressure Simulation Method Based on Multi-mode Acceleration and Backstepping Sliding Mode [J]. | Acta Armamentarii , 2024 , 45 (6) : 1776-1786 . |
| MLA | Lin, Zhonglin et al. "Aero-engine Compressor Pressure Simulation Method Based on Multi-mode Acceleration and Backstepping Sliding Mode" . | Acta Armamentarii 45 . 6 (2024) : 1776-1786 . |
| APA | Lin, Zhonglin , Wang, Haitao , Liu, Wenchao , Gan, Jinyu , Zhang, Tianhong , Huang, Feng . Aero-engine Compressor Pressure Simulation Method Based on Multi-mode Acceleration and Backstepping Sliding Mode . | Acta Armamentarii , 2024 , 45 (6) , 1776-1786 . |
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Abstract ：
This study proposes an active surge control method based on deep reinforcement learning to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an aeroengine. Initially, the study establishes the compressor dynamic model with uncertainties, disturbances, and Close-Coupled Valve (CCV) actuator delay. Building upon this foundation, a Partially Observable Markov Decision Process (POMDP) is defined to facilitate active surge control. To address the issue of unobservability, a nonlinear state observer is designed using a finite-time high-order sliding mode. Furthermore, an Improved Soft Actor- Critic (ISAC) algorithm is developed, incorporating prioritized experience replay and adaptive temperature parameter techniques, to strike a balance between exploration and convergence during training. In addition, reasonable observation variables, error-segmented reward functions, and random initialization of model parameters are employed to enhance the robustness and generalization capability. Finally, to assess the effectiveness of the proposed method, numerical simulations are conducted, and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control (SOSMC) method. The simulation results demonstrate that the deep reinforcement learning based controller outperforms other methods in both tracking accuracy and robustness. Consequently, the proposed active surge controller can effectively ensure stable operation of compressors in the high-pressure-ratio and high-efficiency region. (c) 2024 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword ：
Active surge control Active surge control Aeroengine surge Aeroengine surge Deep reinforcement learning Deep reinforcement learning Moore-Greitzer model Moore-Greitzer model Nonlinear observer Nonlinear observer Soft actor-critic Soft actor-critic
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| GB/T 7714 | Zhang, Xinglong , Lin, Zhonglin , Ji, Runmin et al. Deep reinforcement learning based active surge control for aeroengine compressors [J]. | CHINESE JOURNAL OF AERONAUTICS , 2024 , 37 (7) : 418-438 . |
| MLA | Zhang, Xinglong et al. "Deep reinforcement learning based active surge control for aeroengine compressors" . | CHINESE JOURNAL OF AERONAUTICS 37 . 7 (2024) : 418-438 . |
| APA | Zhang, Xinglong , Lin, Zhonglin , Ji, Runmin , Zhang, Tianhong . Deep reinforcement learning based active surge control for aeroengine compressors . | CHINESE JOURNAL OF AERONAUTICS , 2024 , 37 (7) , 418-438 . |
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Abstract ：
Unmanned Aerial Vehicles (UAVs) are playing an increasingly critical role in military surveillance missions. However, due to safety and economic issues, it is necessary to validate the UAV performance and algorithms in a semi-physical simulation environment before the real flights. In this study, the mechanical structure and control system of the semi-physical simulation system is developed for the UAVs. In order to better simulate the attitude of the UAV during a flight, the flight attitude simulator is designed. Also, a sand table of the mountainous scenery from real inspection tasks is developed to work with the simulation system. Path planning algorithms are embedded in the platform, and improvements and evaluations of traditional algorithms are carried out. Then, an improved adaptive particle swarm optimization (IAPSO) algorithm is proposed to improve the accuracy of the path of UAVs. An energy-consumption prediction model is established for the platform. By combining the IAPSO algorithm with the energy-consumption prediction model, the best path with low energy consumption can be obtained. Finally, the effectiveness of the simulation system is verified by comparison experiments of the mathematical simulation, semi-physical simulation, and real flight.
Keyword ：
Energy efficiency Energy efficiency Path planning Path planning Semi-physical simulation system Semi-physical simulation system Unmanned Aerial Vehicle Unmanned Aerial Vehicle
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| GB/T 7714 | Lin, Zhonglin , Li, Yufeng , Qian, Qiang et al. Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery [J]. | COMPUTERS & INDUSTRIAL ENGINEERING , 2024 , 197 . |
| MLA | Lin, Zhonglin et al. "Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery" . | COMPUTERS & INDUSTRIAL ENGINEERING 197 (2024) . |
| APA | Lin, Zhonglin , Li, Yufeng , Qian, Qiang , Huang, Feng , Zhang, Xinglong , Zhang, Tianhong et al. Construction of an improved semi-physical simulation system for UAV with integrated energy-consumption prediction model and its evaluation of the path planning algorithms in mountainous scenery . | COMPUTERS & INDUSTRIAL ENGINEERING , 2024 , 197 . |
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Abstract ：
This study proposes an active surge control method based on deep reinforcement learn-ing to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an aeroengine.Initially,the study establishes the compressor dynamic model with uncertainties,disturbances,and Close-Coupled Valve(CCV)actuator delay.Building upon this foundation,a Partially Observable Markov Decision Process(POMDP)is defined to facilitate active surge control.To address the issue of unobservability,a nonlinear state observer is designed using a finite-time high-order sliding mode.Furthermore,an Improved Soft Actor-Critic(ISAC)algorithm is developed,incorporating prioritized experience replay and adaptive tem-perature parameter techniques,to strike a balance between exploration and convergence during training.In addition,reasonable observation variables,error-segmented reward functions,and ran-dom initialization of model parameters are employed to enhance the robustness and generalization capability.Finally,to assess the effectiveness of the proposed method,numerical simulations are conducted,and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control(SOSMC)method.The simulation results demonstrate that the deep rein-forcement learning based controller outperforms other methods in both tracking accuracy and robustness.Consequently,the proposed active surge controller can effectively ensure stable opera-tion of compressors in the high-pressure-ratio and high-efficiency region.
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| GB/T 7714 | Xinglong ZHANG , Zhonglin LIN , Runmin JI et al. Deep reinforcement learning based active surge control for aeroengine compressors [J]. | 中国航空学报（英文版） , 2024 , 37 (7) : 418-438 . |
| MLA | Xinglong ZHANG et al. "Deep reinforcement learning based active surge control for aeroengine compressors" . | 中国航空学报（英文版） 37 . 7 (2024) : 418-438 . |
| APA | Xinglong ZHANG , Zhonglin LIN , Runmin JI , Tianhong ZHANG . Deep reinforcement learning based active surge control for aeroengine compressors . | 中国航空学报（英文版） , 2024 , 37 (7) , 418-438 . |
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Abstract ：
在航空发动机硬件在环仿真试验中,压气机出口压力模拟是一个重要的环节.为模拟压气机失稳过程中的气流突变现象,需要设计一种高精度的压力模拟系统,通过该模拟系统生成真实压力信号为全权限数字发动机控制器提供准确的压力激励.根据开关阀特性提出多模式加速切换策略,设计加速驱动波形及生成方法,结合现场可编程门阵列模块精确输出加速波形,提高开关阀启闭速度;在传统三模式与五模式切换的基础上设计七模式切换方法,结合反步滑模控制方法提高控制精度并证明了控制系统的稳定性.研究结果表明:在稳态压力测试中最大平均超调量为1.25%,最大平均稳态误差为 0.016 MPa;在随机阶跃响应试验与正弦跟踪试验中仅有低于 0.02 MPa的平均误差;压力模拟方法可满足航空发动机硬件在环仿真试验的高精度压力模拟需求,低成本的实现精准模拟压力.
Keyword ：
压力模拟 压力模拟 压气机 压气机 反步滑模控制 反步滑模控制 开关阀 开关阀 硬件在环仿真 硬件在环仿真
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| GB/T 7714 | 林忠麟 , 王海涛 , 刘文超 et al. 基于多模式加速及反步滑模的航空发动机压气机压力模拟方法 [J]. | 兵工学报 , 2024 , 45 (6) : 1776-1786 . |
| MLA | 林忠麟 et al. "基于多模式加速及反步滑模的航空发动机压气机压力模拟方法" . | 兵工学报 45 . 6 (2024) : 1776-1786 . |
| APA | 林忠麟 , 王海涛 , 刘文超 , 甘锦裕 , 张天宏 , 黄峰 . 基于多模式加速及反步滑模的航空发动机压气机压力模拟方法 . | 兵工学报 , 2024 , 45 (6) , 1776-1786 . |
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Pressure servo control plays a crucial role in the majority of industrial applications that use compressed air as their power source. How to reduce the energy consumption of the pneumatic system while ensuring the high accuracy of pressure control remains a key problem to be solved. This paper proposes a Gaussian Process (GP) 1 model predictive controller (MPC) applied to a pressure servo control system based on high-speed on-off valves. The smallest quantity of collected data is used to create the GP model. The entire model, which includes some predictive data, is produced once the generated model has been optimized. The GP model is combined with the MPC to perform comparative experiments using an industrial controller with a Field Programmable Gate Array (FPGA). The system responds quickly and with little tracking error in steady-state response studies as well as dynamic response experiments. The GP-MPC achieves the best outcomes in the dynamic comparison tests. The root mean square error (RMSE) is just 2.42 kPa, and the overshoot is less than 59.9% of the Proportional-Integral-Derivative (PID) controller and 68.5% of the sliding mode controller (SMC). Although the compressed air con-sumption of GP-MPC is basically the same as that of PID, it is significantly better than SMC, with a total saving of about 70.2%. All the experiments prove that the controller proposed in this paper can effectively improve the energy efficiency and tracking accuracy of the pressure servo control system.
Keyword ：
Energy efficiency Energy efficiency Gaussian process Gaussian process Model predictive control Model predictive control Pneumatic system Pneumatic system Pressure tracking Pressure tracking
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| GB/T 7714 | Lin, Zhonglin , Gan, Jinyu , Qian, Qiang et al. Developing a novel Gaussian process model predictive controller to improve the energy efficiency and tracking accuracy of the pressure servo control system [J]. | JOURNAL OF CLEANER PRODUCTION , 2023 , 417 . |
| MLA | Lin, Zhonglin et al. "Developing a novel Gaussian process model predictive controller to improve the energy efficiency and tracking accuracy of the pressure servo control system" . | JOURNAL OF CLEANER PRODUCTION 417 (2023) . |
| APA | Lin, Zhonglin , Gan, Jinyu , Qian, Qiang , Huang, Feng , Zhang, Xinglong , Zhang, Tianhong et al. Developing a novel Gaussian process model predictive controller to improve the energy efficiency and tracking accuracy of the pressure servo control system . | JOURNAL OF CLEANER PRODUCTION , 2023 , 417 . |
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In the process of unmanned aerial vehicle (UAV) visual-navigation-algorithm design and accuracy verification, the question of how to develop a high-precision and high-reliability semi-physical simulation platform has become a significant engineering problem. In this study, a new UAV semi-physical-simulation-platform architecture is proposed, which includes a six-degree-of-freedom mechanical structure, a real-time control system and real-time animation-simulation software. The mechanical structure can realistically simulate the flight attitude of a UAV in a three-dimensional space of 4 x 2 x 1.4 m. Based on the designed mechanical structure and its dynamics, the control system and the UAV real-time flight-animation simulation were designed. Compared with the conventional simulation system, this system enables real-time flight-attitude simulation in a real physical environment and simultaneous flight-attitude simulation in virtual-animation space. The test results show that the repeated positioning accuracy of the three-axis rotary table reaches 0.006 degrees, the repeated positioning accuracy of the three-axis translation table reaches 0.033 mm, and the dynamic-positioning accuracy reaches 0.04 degrees and 0.4 mm, which meets the simulation requirements of high-precision visual UAV navigation.
Keyword ：
optical flow optical flow semi-physical simulation platform semi-physical simulation platform six degrees of freedom six degrees of freedom unmanned aerial vehicle unmanned aerial vehicle visual navigation visual navigation
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| GB/T 7714 | Lin, Zhonglin , Wang, Weixiong , Li, Yufeng et al. Design and Experimental Study of a Novel Semi-Physical Unmanned-Aerial-Vehicle Simulation Platform for Optical-Flow-Based Navigation [J]. | AEROSPACE , 2023 , 10 (2) . |
| MLA | Lin, Zhonglin et al. "Design and Experimental Study of a Novel Semi-Physical Unmanned-Aerial-Vehicle Simulation Platform for Optical-Flow-Based Navigation" . | AEROSPACE 10 . 2 (2023) . |
| APA | Lin, Zhonglin , Wang, Weixiong , Li, Yufeng , Zhang, Xinglong , Zhang, Tianhong , Wang, Haitao et al. Design and Experimental Study of a Novel Semi-Physical Unmanned-Aerial-Vehicle Simulation Platform for Optical-Flow-Based Navigation . | AEROSPACE , 2023 , 10 (2) . |
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