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2023 China automation Congress, CAC 2023

作者： Yihan, Liu Dong, Sun Han, Yan Quanrun, Mu Xiaochao, Liu Pengyuan, Qi School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Institute of Control Engineering National Key Laboratory of Space Intelligent Control Beijing100094 China Beijing Sun Wise Space Technology Ltd. Beijing100190 China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (纸本)9798350303759

With the rapid advancements in space offensive and defensive countermeasure technologies, the scope of international confrontation has expanded beyond the traditional domains of sea, land, and air, reaching into the realm of space. In modern military warfare, the focus has shifted from solely physical and informational aspects to include the cognitive domain. Within this domain, intent recognition emerges as a crucial technology, offering tactical insights and decision-making guidance for future wars. This paper presents an intention recognition algorithm for space target behaviors, utilizing a long short-term memory (LSTM) network. By incorporating time series motion parameters and the relative relationships between enemy and friendly satellite, the algorithm identifies the tactical intentions behind space target orbital behaviors. Additionally, a space target attack and defense scenario simulation environment is established using MATLAB and C-W equations, enabling the creation of a dataset for space target recognition. Through the proposed method, the simulation effectively discerns the orbital behavior intentions of space targets, providing valuable guidance for battlefield decision-making. © 2023 IEEE.

关键词： Long short-term memory

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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A Norm Optimal Iterative Learning control Approach with Efficient Computation 13

A Norm Optimal Iterative Learning Control Approach with Effi...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Gao, Luyuan Zhuang, Zhihe Tao, Hongfeng Chen, Yiyang Paszke, Wojciech Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (纸本)9798350361674

Iterative learning control (ILC) is excellently applied to output tracking for systems that perform repetitive tasks. Classical norm optimal ILC (NOILC) approach yields optimal control inputs by minimizing a quadratic cost function. However, this approach requires the introduction of supervectors, i.e., lifting technique, for system description, which raises a high computational load problem. The contribution of this paper is to propose a non-lifted NOILC (N-NOILC) approach by redesigning a cost function that does not involve the lifting technique, achieving the goal of efficient computation. Therefore, the proposed approach is particularly suitable for large tracking tasks with long trial duration and high sampling frequency. Moreover, considering the model uncertainty in practice, robust convergence is analyzed. Numerical simulations are shown to demonstrate the properties and advantages of the proposed approach. © 2024 IEEE.

关键词： Adversarial machine learning

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Linear parameter-varying model predictive control for nonlinear systems using general polytopic tubes

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Automatica 2024年 160卷

作者： Abbas, Hossam Seddik Institute for Electrical Engineering in Medicine University of Lübeck Moislinger Allee 53-55 Lübeck23558 Germany Automation and Control Group Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstraße 2 Kiel24143 Germany

This paper deals with model predictive control (MPC) for nonlinear systems using linear-parameter varying (LPV) embedding of the nonlinear dynamics (LPVMPC). The proposed LPVMPC can incorporate information of the future evolution of the scheduling parameter over the MPC prediction horizon with uncertainty bounds, which are used to construct anticipated scheduling tubes for robustification. Therefore, this approach is less conservative than the methods that only consider knowledge of the bounds on the scheduling parameter's rate of variation. The scheduling tubes are employed to synthesize online general polytopic invariant state tubes. The optimization problem of the proposed LPVMPC is a single quadratic program. Recursive feasibility is proven. A numerical example is presented for demonstrating the effectiveness of the proposed LPVMPC algorithm compared to nonlinear MPC and other standard approaches. © 2023 Elsevier Ltd

关键词： Model predictive control

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Point to Point Trajectory Planning of Permanent Magnet Spherical Motor Based on Quintic Polynomial Interpolation 26

Point to Point Trajectory Planning of Permanent Magnet Spher...

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26th International Conference on electrical Machines and Systems, ICEMS 2023

作者： Tan, Ao Guo, Xiwen Wang, Qunjing Sun, Yuming Yang, Qiyong Zhang, Ronglin Anhui University School of Electrical Engineering and Automation Hefei China Anhui University National Engineering Laboratory of Energy-saving Motor & Control Technique Hefei China Anhui University Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control Hefei China State Grid Lu'an Electric PowerSupply Company Lu'an China

ISBN: (纸本)9798350317589

Aiming at the abrupt change of velocity and angular acceleration in point-to-point operation of permanent magnet spherical motor(PMSM), a trajectory planning scheme based on Quintic Polynomial Interpolation(QPI) was proposed. Firstly, the basic structure and driving principle of PMSM are introduced. The torque control of PMSM is realized by rotor dynamic modeling combined with current driving. Then, the kinematics characteristics of the QPI algorithm are analyzed, and the interpolation coefficients of the QPI algorithm are solved by combining six constraints including velocity and acceleration. Finally, PD controller is used to control the PMSM, and smooth point-to-point trajectory planning is achieved. The simulation results show that the trajectory planning algorithm proposed in this paper not only ensures the high-precision trajectory tracking control, but also improves the smoothness of rotor running speed and reduces the system impact, which can provide a reference for the practical driving application of PMSM in the next step. © 2023 IEEE.

关键词： Permanent magnets

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automation System Architecture of pH Neutralization Process in Batik Wastewater Treatment Plant 4

Automation System Architecture of pH Neutralization Process ...

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4th engineering Physics International Conference 2023, EPIC 2023

作者： Diyah, Nazuwatussya Ekawati, E. Pradipta, J. Yulia, E. Master Program of Instrumentation and Control Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Engineering Physics Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Center for Instrumentation Technology and Automation Institut Teknologi Bandung Indonesia

Batik production in home industries needs support wastewater treatment in a limited space. Therefore, a modular-type, lab-scale wastewater treatment plant (WWTP) with a capacity of 87.75 liters and a dimension of 1.5 m × 0.75 m × 1 m has been constructed. The WWTP combines three significant processes: equalization, neutralization, and adsorption. This study focuses on the monitoring systems for the pH neutralization process. The system uses four microprocessors in a Master/Slave architecture and the I2C protocols for data communication between processors. The measuring instruments include pH, temperature, turbidity, and flow rate sensors. The actuators consist of submersible and peristaltic pumps. The operating range of pH, temperature, turbidity, and flow rate sensors are 0-14, 25-81.44℃, 0-3000 NTU, and 6.49-20.84 ml/s respectively. The pumps work in Pulse Width Modulation (PWM) mode with an operating range of 125-225 PWM. All sensors and actuators operate linearly within the operating range with a correlation coefficient of 0.97-1. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Turbidity

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Predefined-Time Attitude Tracking control for Quadrotor UAV with Input Delay

Predefined-Time Attitude Tracking Control for Quadrotor UAV ...

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2023 China automation Congress, CAC 2023

作者： Li, Shuo Pei, Hailong Duan, Na College of Automation Science and Enginneering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou510640 China School of Automation Science and Enginneering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou510640 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China

ISBN: (纸本)9798350303759

In this paper, the predefined time attitude tracking flight problem for a quadrotor unmanned aerial vehicle (UAV) system with input delays is investigated. By means of a novel auxiliary system, the effects of input delay are solved. Then, on the basis of the backstepping method and predefined performance control technology, the predefined-time attitude tracking controller is constructed to make UAV attitude tracking. Simulation results verify the validity of the proposed scheme. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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Time-Constrained Cooperative Guidance Based on Deep Neural Network 42

Time-Constrained Cooperative Guidance Based on Deep Neural N...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Changhai Yu, Jianglong Dong, Xiwang Li, Qingdong Ren, Zhang Wang, Feifei Hu, Xijing School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Institute of Control and Electronic Technology Beijing100038 China

ISBN: (纸本)9789887581543

Time-constrained cooperative guidance based on deep neural network (DNN) is developed to address the problem of cooperative guidance of multiple missiles against maneuvering targets. Firstly, deep neural network is applied to estimate time-to-go. A simplified dataset is created to improve the estimation effect and the performance of training results is good. Secondly, time-constrained cooperative guidance law is designed. Time-constrained guidance (TCG) law based on variable navigation ratio is proposed and an optimal method of cooperative time is designed. Thirdly, simulation in 3-D space and equivalent experiment in 2-D space are carried out to verify the performance of guidance law. An equivalent experiment platform based on multiple unmanned ground vehicles (Multi-UGVs) is erected and the experiment to achieve cooperative guidance is carried out. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Deep neural networks

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Policy Iteration Adaptive Dynamic programming Based control for Hypersonic Flight Vehicles

Policy Iteration Adaptive Dynamic Programming Based Control ...

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2023 China automation Congress, CAC 2023

作者： Zhang, Yuanhang Fu, Jingbo Liang, Chen Wang, Chenliang Qiao, Jianzhong School of Automation Science and Electrical Engineering Beihang University Beijing China China Academy of Aerospace Science and Industry Launch Vehicle Technology Beijing China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9798350303759

A novel approximate optimal tracking control scheme using policy iteration adaptive dynamic programming is proposed for hypersonic flight vehicles. By analysing the uniqueness of the equilibrium point during stable level flight of a hypersonic flight vehicle after achieving tracking objective, the output tracking problem is transformed into a state regulation problem. The initial admissible control law is obtained based on feedback linearization and sliding mode control under the discrete quadratic form cost function. Policy iteration adaptive dynamic programming algorithm is used to design the approximate optimal control law and the neural network approximation method is applied to approach the cost function. Simulation results illustrate the effectiveness of the proposed scheme. © 2023 IEEE.

关键词： Cost functions

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