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Chinese control Conference (CCC)

作者： Lei Cao Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the tracking control problem for networked control system (NCS) under communication delays is investigated. In order to realize the tracking of the NCS, an event-triggered predictive control strategy is provided to compensate for the delays and achieve stability. Both tracking performance and input increments are considered in the proposed cost function. Based on the incremental inputs, an event-triggered mechanism is established to reduce the communication costs. The efforts of different parameters are illustrated through the results of the simulation, and the effectiveness of the proposed control scheme is also validated from the application of the networked lighting setup.

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Disturbance Observer-Based High-Order Fully Actuated Robust control of Uncertain Robotic Systems

Disturbance Observer-Based High-Order Fully Actuated Robust ...

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Fully Actuated System theory and Applications (CFASTA), Conference on

作者： Fuxing Zhu Yue Zhao Zhuang Liu Fei Yan Jianxing Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China School of Information Science and Technology Southwest Jiaotong University Chengdu P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Model uncertainties and external disturbances significantly impact the performance of trajectory tracking control in robotic systems. This paper proposes a novel control strategy based on high-order fully actuated (HOFA) system theoretical methods. The robotic system with nonlinear properties is transformed into a closed-loop linear system by pseudo-linear parameter design. The tracking controller is designed for the linearized system by combining robust control methods. Recognizing the system’s susceptibility to disturbances during operation, a nonlinear disturbance observer (NDO) is designed to estimate and compensate for the lumped disturbances, and the stability of the system is demonstrated. Finally, comparative simulations based on the UR5 robot validate the effectiveness of the proposed control strategy.

关键词： Robust control Linear systems Uncertainty Trajectory tracking control systems Mathematical models Stability analysis

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Deep learning-based defense and detection scheme against eavesdropping and typical cyber-physical attacks

Deep learning-based defense and detection scheme against eav...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Wu, Shimeng Jiang, Yuchen Luo, Hao Li, Xianling Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Science and Technology on Thermal Energy and Power Laboratory Wuhan430205 China

ISBN: (纸本)9781665401159

Cyber-physical systems (CPSs) play an increasingly important role in critical industrial infrastructure. However, the use of communication networks leads to vulnerabilities in systems. Considering the negative effects caused by the recent attack events, guaranteeing safe and reliable operation of infrastructures is necessary. This paper proposes a dual denoising auto-encoder (DDAE) and based on which proposes a unified scheme (in the sense of sharing the DDAE models) to protect cyber-physical systems (CPS) from being eavesdropped, and to detect the typical attacks, i.e., false data injection (FDI) attacks, denial-of-service (DoS) attacks, and replay attacks. The industrial data are encrypted by the encoder of the offline-trained DDAE before transmission, and at the receiving end, the transmitted data are analyzed by a residual generator-based detection module. If the received data are trustworthy, they will be decrypted and used in the subsequent industrial processes. Otherwise, attack warnings will be triggered. The simulation results on the IEEE-57 bus system show that the proposed scheme can effectively prevent the transmitted data from being eavesdropped and can accurately detect typical cyber-physical attacks. © 2021 IEEE.

关键词： Cyber Physical System

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Robust fault detection approach for switched time-delay systems by using sliding mode observer

Robust fault detection approach for switched time-delay syst...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Ali, Shafqat Jiang, Yuchen Faisal, Shah Luo, Hao Li, Xianling Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Science and Technology on Thermal Energy and Power Laboratory Wuhan430205 China

ISBN: (纸本)9781665401159

This study focuses on observer-based fault detection for switched systems with state delays. Firstly, for the generation of residual signal, a fault detection sliding mode observer is designed. Afterwards, a robust performance index is applied such that the residual error signal minimizes the impact of faults. Moreover, a Lyapunov function is employed for stability of the switched time-delay systems and the solution leads to a sufficient condition in terms of Linear Matrix Inequalities. Further more residual evaluation and threshold are computed for efficient fault detection. Finally, a numerical example is furnished to verify the performance of the design technique. © 2021 IEEE.

关键词： Fault detection

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Research on Adaptive control of Tunneling Machine Based on PSO-BP Neural Network PID

Research on Adaptive Control of Tunneling Machine Based on P...

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Chinese Automation Congress (CAC)

作者： Ruohan Liu Chunping Ren Degen Li Yongli Liu School of Electrical and Control engineering Safety Engineering College Heilongjiang University of Science and Technology Harbin China School of Mechanical Engineering Heilongjiang University of Science and Technology Harbin China Academic Theory Research Department Heilongjiang University of Science and Technology Harbin China School of Mining engineering Heilongjiang University of Science and Technology Harbin China

In order to improve the response speed and control accuracy of the tunneling machine control system, and to address the limitations of traditional algorithms that are prone to falling into local extremum and weak generalization ability during the solving process, a PSO-BP neural network PID adaptive control method for tunneling machines is proposed. The weights of the BP neural network are optimized by the global search ability of the PSO algorithm. The neural network outputs the fitness values of the PID parameters, and the initial weights of the neural network are updated. The PID parameters are adjusted in realtime. The results show that the unit Step response curve of the system reaches the stable state of the system after 0.85 s in the traditional PID control, while the PSO-BP neural network PID controller only needs 0.4 s to be stable, and the overshoot of the PSO-BP neural network PID controller is far less than that of the traditional PID controller. Therefore, the PSO BP neural network PID controller has good dynamic and static characteristics, which can maximize the generalization ability of the traditional BP neural network and ensure the Rate of convergence and learning ability.

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Spacecraft formation-containment flying control with time-varying translational velocity

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Chinese Journal of Aeronautics 2020年第1期33卷 271-281页

作者： Liangming CHEN Chuanjiang LI Yanning GUO Guangfu MA Baolong ZHU Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering and Automation Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

This paper investigates the problem of Spacecraft Formation-Containment Flying control(SFCFC)when the desired translational velocity is *** SFCFC problem,there are multiple leader spacecraft and multiple follower spacecraft and SFCFC can be divided into leader spacecraft’s formation control and follower spacecraft’s containment ***,under the condition that only a part of leader spacecraft can have access to the desired time-varying translational velocity,a velocity estimator is designed for each leader ***,based on the estimated translational velocity,a distributed formation control algorithm is designed for leader spacecraft to achieve the desired formation and move with the desired translational velocity ***,to ensure all follower spacecraft converge to the convex hull formed by the leader spacecraft,a distributed containment control algorithm is designed for follower ***,to reduce the dependence of the designed control algorithms on the graph information and increase system robustness,the control gains are changing adaptively and the parametric uncertainties are handled,***,simulation results are provided to illustrate the effectiveness of the theoretical results.

关键词： Adaptive gain technique Distributed control Formation-containment Formation control Spacecraft formation flying

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Solving the Model Unavailable MARE using Q-Learning Algorithm

arXiv

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arXiv 2024年

作者： Yan, Fei Gao, Jie Feng, Tao Liu, Jianxing School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

In this paper, the discrete-time modified algebraic Riccati equation (MARE) is solved when the system model is completely unavailable. To achieve this, firstly a brand new iterative method based on the standard discrete-time algebraic Riccati equation (DARE) and its input weighting matrix is proposed to solve the MARE. For the single-input case, the iteration can be initialized by an arbitrary positive input weighting if and only if the MARE has a stabilizing solution;nevertheless a pre-given input weighting matrix of a sufficiently large magnitude is used to perform the iteration for the multi-input case when the characteristic parameter belongs to a specified subset. Benefit from the developed specific iteration structure, the Q-learning (QL) algorithm can be employed to subtly solve the MARE where only the system input/output data is used thus the system model is not required. Finally, a numerical simulation example is given to verify the effectiveness of the theoretical results and the algorithm. © 2024, CC BY.

关键词： Iterative methods

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Methane and nitrous oxide emissions from municipal wastewater treatment plants in China:A plant-level and technology-specific study

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Environmental Science and Ecotechnology 2024年第4期20卷 123-130页

作者： Haiyan Li Liangfang You He Du Bowen Yu Lu Lu Bo Zheng Qiang Zhang Kebin He Nanqi Ren School of Civil and Environmental Engineering Harbin Institute of TechnologyShenzhen518055China Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and EcologyTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China

Wastewater treatment is an important source of greenhouse gases(GHGs).Yet large uncertainties remain in the quantification of GHG emissions from municipal wastewater treatment plants(MWWTPs)in China.A high-resolution and technology-specific emission inventory is still lacking to support mitigation strategies of *** we develop a plant-level and technology-based MWWTP emission inventory for China covering 8703 plants and 19 treatment technology categories by compiling and harmonizing the most up-to-date facility-level ***'s methane(CH_(4))and nitrous oxide(N_(2)O)emissions from MWWTPs in 2020 are estimated to be 150.6 Gg and 22.0 Gg,respectively,with the uncertainty range of-30%to 37%and-30%to 26%at 95%confidence *** find an emission inequality across cities,with the richest cities emitting two times more CH_(4)and N_(2)O per capita from municipal wastewater treatment than the poorest *** emitted CH_(4)and N_(2)O are dominated by Anaerobic/Anoxic/Oxic-,Sequencing Batch Reactor-,Oxidation Ditch-,and Anoxic/Oxic-based MWWTPs of less than 20 years *** the relatively young age structure of China's MWWTPs,the committed emissions highlight the importance of reducing on-site GHG emissions by optimization of operating conditions and innovation *** emission differences among our estimates,previous studies,and the Intergovernmental Panel on Climate Change guidelines are largely attributed to the uncertainties in emission factors,implying the urgent need for more plant-integrated measurements to improve the accuracy of emission accounting.

关键词： Methane Nitrous oxide Emissions Municipal wastewater treatment plants

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Battery Management for Warehouse Robots via Average-Reward Reinforcement Learning

Battery Management for Warehouse Robots via Average-Reward R...

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IEEE International Conference on Robotics and Biomimetics

作者： Yongjin Mu Yanjie Li Ke Lin Ki Deng Qi Liu Department of Control Science and Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)9781665481106

In automated warehouses, the battery management strategy of Automated Guided Vehicles (AGVs) can affect the throughput and operational efficiency of the warehouse. In this paper, we first model the battery management problem as a Markov Decision Process (MDP) and adopt the deep reinforcement learning (DRL) algorithm as the battery management strategy. However, discounted reward DRL algorithms ignore long-term benefits, which are not suitable for the strategy since orders arriving at the warehouse at every moment are important and should be treated. In order to solve the above problems, we then introduce the average reward DRL algorithm to focus more on long-term benefits. But the existing average reward DRL algorithms have the problems of low sample utilization and unstable training. Therefore, we present a practical algorithm called average reward TD3 (ARTD3) that learns faster and is more stable. Finally, we conduct extensive experiments to confirm that ARTD3 outperforms discounted reward DRL algorithm and rule-based methods.

关键词： Training Measurement Deep learning Remotely guided vehicles Battery management systems Reinforcement learning Markov processes

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Data-driven SOC Estimation with Moving Window Adaptive Residual Generator for Li-ion Battery

Data-driven SOC Estimation with Moving Window Adaptive Resid...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Xu, Xiaoyi Luo, Hao Wang, Hao Chow, Mo-Yuen Huang, Cong-Sheng Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9781665401159

Lithium battery has been widely used in electric vehicle (EV) and microgrid applications as an outstanding energy storage device. To optimally control the batteries, the nonlinear battery dynamics and the battery State-of-Charge (SOC) need to be real-time and accurately estimated by the battery management systems (BMSs). Compared with traditional estimation algorithms, the observer-based adaptive residual generator has the advantages of small calculation amount, stability, and adaptability. It can accurately estimate the SOC of a battery with a high excited load current. However, in the case of a low excited load current, the SOC estimation effect is poor. Besides, as a data-driven algorithm, the adaptive residual generator's estimation effect will deteriorate when affected by measurement noise. This paper proposes a moving window scheme for the adaptive residual generator and analyzes the parameter setting range to ensure the stability and convergence of the algorithm, which provides guidance for parameter selection when applying the algorithm. On the one hand, the proposed moving window scheme effectively reduces the high excitation requirement of the adaptive residual generator for the load current. Even under low excitation, the moving window adaptive residual generator can still accurately estimate the SOC. On the other hand, the addition of a moving window effectively improves the anti-disturbance ability of the adaptive residual generator. The performance of the proposed method is verified by lithium battery simulation experiments. © 2021 IEEE.

关键词： Lithium-ion batteries

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