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LQG control for sampled-data systems under stochastic sampling

Journal of the Franklin Institute 2020年第5期357卷 2773-2790页

作者： Haoyuan Sun Jian Sun Jie Chen Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 PR China College of Electronic and Information Tongji University Shanghai China

This paper studies the linear-quadratic-Gaussian (LQG) problem for sampled-data systems with a stochastic sampling interval obeying a certain probability distribution. An optimal estimator of the system state is presented by the standard Kalman filter , and the Vandermonde matrix and Kronecker product operation are used to calculate the mathematical expectation caused by stochastic sampling in the process of designing the LQG controller. Moreover, it was proved that the controller can ensure the system is exponentially mean square stable. Finally, some simulation results are given to verify the effectiveness and practicability of the proposed controller design method.

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Distributed MPC Algorithm for Linear Discrete systems with Communication Noise

Distributed MPC Algorithm for Linear Discrete Systems with C...

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第40届中国控制会议

作者： Duqiao Zhao Ding Liu Linxiong Liu Xia Zhang National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In this paper, a distributed MPC algorithm is proposed for linear discrete systems with bounded communication noise and random communication failure. Based on the characteristics of time-varying directed graphs, the convergence of the algorithm is analyzed in the case of communication noise and random communication failure. This algorithm uses the distributed projection dual subgradient algorithm to solve the dual problem and replaces the double stochastic condition of the traditional algorithm with row stochastic condition. The exponential stability of the system is guaranteed. Finally, an example is given to illustrate the performance of the algorithm.

关键词： Distributed MPC Bounded Communication Noise Random Communication Faults Distributed Projection Dual Subgradient Row Stochastic

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Non-Fourier Heat Conduction based on Self-Adaptive Weight Physics-Informed Neural Networks

Non-Fourier Heat Conduction based on Self-Adaptive Weight Ph...

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第40届中国控制会议

作者： Shuyan Shi Ding Liu Zhongdan Zhao National & Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shananxi Key Laboratory of Complex System Control and Intelligent Information Processing

This paper establishes the non-Fourier heat conduction model to describe the heat transfer process of mono-crystalline silicon under the condition of unstable thermal field and thermal shock in the Czochralski method.A novel differential equations solver called Physics-Informed Neural Networks（PINN） algorithm was *** with finite element method（FEM）,this method has some advantages like no grid requirement and easily *** order to deal with the unbalance of constraint condition and speed up the convergence,we propose a novel method called Self-Adaptive Weight Physics-Informed Neural Networks（SWPINN）.The comparison of the experimental results of SWPINN and COMSOL verifies the effectiveness of *** modifying the parameter of non-Fourier heat conduction model,this paper obtains the temperature distribution under different heat relaxation ***,comparison between SWPINN and PINN shows that the proposed method has faster convergence speed and higher accuracy.

关键词： Non-Fourier Heat Conduction Physics-Informed Neural Networks Partial Differential Equation Monocrystalline Silicon Czochralski Method

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Active Vibration Isolation for A Parallel Wheel-legged Robot Based on Dynamic Model

Research Square

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Research Square 2020年

作者： Guo, Fei Wang, Shoukun Yue, Binkai Wang, Junzheng State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing100081 China

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Transient Electromagnetic Transmitter Circuit with Speedy Shutoff Based on Passive Constant Voltage Clamping

Transient Electromagnetic Transmitter Circuit with Speedy Sh...

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第40届中国控制会议

作者： Guangjun Wang Chengxiang Ding Lan Cui Zhaohui Wang Hui Jiang Xinzhao Qi Deshun Min School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System School of Law Zhongnan University of Economics and Law

The parasitic inductance of transmitting coil in transient electromagnetic transmitter causes some serious problems like the overlong falling edge time, overshoot and oscillation of the emission current. In order to solve these above problems, this paper proposes a design scheme of passive constant voltage clamping circuit of highly speeding shutoff based on TVS（Transient Voltage Suppressor） and switch. In this design scheme, resistance, TVS and switch are configured in parallel and then connected into the main launch bridge circuit. During the earlier stage of the shutoff of the emission current, TVS is utilized to form the high voltage clamping and thus realize the highly speeding shutoff of the emission current. At the later stage of the emission current decline, the energy of the load inductance is set free by the resistance to prevent the overshoot and oscillation of the emission current. In this paper, the operating procedure and principle of the circuit, the influence of the parameters of core devices on the circuit performance, and the effectiveness of this design scheme is verified by simulation and experiment. The results of simulation and experiment show that this circuit is effective to reduce the shutoff time of the emission current, restrain the overshoot and oscillation meanwhile, and hence improve the wave quality of the emission current.

关键词： transient electromagnetic TVS passive constant voltage clamping speedy shutoff

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Model Free Adaptive control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Model Free Adaptive Control Algorithm based on ReOSELM for A...

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第40届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhichao Wang Mingyu Fan Bolin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology The College of Computer Science and Artificial Intelligence Wenzhou University

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle（ADV）. As is known, historical data collected from ADV contains valuable information about control systems,therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine（ReOSELM） is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.

关键词： data-driven control autonomous driving vehicle model free adaptive control regularized online sequential extreme learning machine

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High-Flexibility Locomotion and Whole-Torso control for a Wheel-Legged Robot on Challenging Terrain*

High-Flexibility Locomotion and Whole-Torso Control for a Wh...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kang Xu Shoukun Wang Xiuwen Wang Junzheng Wang Zhihua Chen Daohe Liu Key Laboratory of Drive and Control of Servo Motion System Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose a parallel six-wheel-legged robot that can traverse irregular terrain while carrying objectives to do heavy-duty work. This robot is equipped with six Stewart platforms as legs and tightly integrates the additional degrees of freedom introduced by the wheels. The presented control strategy with physical system used to adapt the diverse degrees of each leg to irregular terrain such that robot increases the traversability, and simultaneously to maintain the horizontal whole-torso pose. This strategy makes use of Contact Scheduler (CS) and Whole-Torso control (WTC) to control the multiple degrees of freedom (DOF) leg for performing high-flexibility locomotion and adapting the rough terrain like actively parallel suspension system. We conducted experiments on flat, slope, soft and sand-gravel surface, which validate the proposed control method and physical system. Especially, we attempt to traverse over sand-gravel terrain with 3 people about 240kg payload.

关键词： Legged locomotion Wheels Torso Force Kinematics

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Qing Jiao, Tingyang Ma, Yinxing Zhang, Youmin Wu, Xiaohui Wu, Pengyue Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China The Faculty of Computing Science The University of Alberta EdmontonABT6G 2R3 Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi 'an University of Technology Xi'an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada Huaneng Weihai Power Generation Co. Ltd Weihai264200 China Xi 'an Thermal Power Research Institute Co. Ltd Xi'an710048 China

ISBN: (纸本)9781665426473

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system. © 2021 IEEE

关键词： Long short-term memory

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Parameter estimation of time fractional heat conduction equation for monocrystalline silicon

Parameter estimation of time fractional heat conduction equa...

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Chinese Automation Congress (CAC)

作者： Shuyan Shi Ding Liu Zhongdan Zhao Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shananxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665426480

In this paper, a time-fractional heat conduction model is established to describe the heat transfer process of monocrystalline silicon in the Czochralski method. The numerical solution of the fractional-order model is obtained by using implicit difference scheme. In addition, an improved grey wolf algorithm is proposed to estimate the parameters of the fractional heat conduction model using the experimental temperature data. The numerical experiment results show that the time-fractional heat conduction model has a higher explanatory ability, compared with the classical heat conduction model. The Improved Grey Wolf algorithm proposed in this paper is effective in solving the parameter estimation problem of the time-fractional heat conduction equation.

关键词： Heating systems Temperature distribution Parameter estimation Inverse problems Simulation Mathematical models Silicon

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