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The Optimal Duty Cycle Model-Predictive control of PMSM Used for Satellite Payload 1

17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Li, Jialin Chen, Zhen Hu, Hengzai Liu, Xiangdong Bao, Liang School of Automatic Beijing Institute of Technology Beijing100081 China Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision for Complex System Beijing100081 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (数字)9789819904082

ISBN: (纸本)9789819904075

Model-predictive control is emerging as an advanced control strategy for its feature that the state variables can be predicted according to the math model of the control system. In the PMSM drive application, conventional model-predictive control strategy induces current ripple due to the fact that the optimal voltage vector always acts on the system for the whole control period. In order to restrain the current ripple, the idea of duty cycle control is taken into MPC by inserting a zero vector into the optimal non-zero vector and regulating the duty cycle of non-zero vector. In this paper, the optimal duty cycle FSC-MPC strategy based on two different principles is investigated. Principle A is that the estimation is equal to the reference at the end of control cycle, and principle B is that the area under trace of the estimation is equal to that of reference. The calculation of duty based on the two different principles and the flow chart of the strategy are introduced in detail. Finally, the effectiveness of the optimal duty cycle FSC-MPC is firstly verified by the simulation, and then verified by the experimental results, which is implemented on the PMSM driver of the DQ-1 satellite payload. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Model predictive control

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Research on Path Planning of Mobile Robot Based on DWA-IMP-A∗ Algorithm 42

Research on Path Planning of Mobile Robot Based on DWA-IMP-A...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Zhi Zhang, Lin Wang, Shoukun Niu, Tianwei Xu, Yongkang Cao, Jin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology Beijing100081 China

ISBN: (纸本)9789887581543

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A∗ algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A∗ combined with reverse path and dynamic window method (DWA-IMP-A∗) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A∗ algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： motion planning

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Event-triggered learning of Euler-Lagrange systems: A Koopman operator framework

Event-triggered learning of Euler-Lagrange systems: A Koopma...

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IEEE Conference on Decision and control

作者： Kaikai Zheng Dawei Shi Shilei Li Yang Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Mechanical Engineering Faculty of Engineering University of Victoria Victoria BC Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Euler-Lagrange (EL) systems represent a crucial and large class of dynamical systems, and a precise model of the true system would be beneficial in planning and tracking problems. This work aims to learn an unknown EL system using noisy measurement data to achieve improved data utilization efficiency. Specifically, for the considered EL system, a linear representation of the system is constructed using the Koopman operator, which is further characterized by sample data using Willems’ fundamental lemma. Moreover, an event-triggered learning mechanism is proposed to improve data utilization efficiency, and it is designed based on the analysis of the learning error bounds. The effectiveness of the proposed event-triggered learning approach is validated through a manipulator example.

关键词： Learning systems Event detection Manipulators Safety Planning Noise measurement Dynamical systems

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Active Damping control Used for servo system of Satellite Payload with Deteriorating Friction

Active Damping Control Used for Servo System of Satellite Pa...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Jialin Li Zhen Chen Hengzai Hu Xiangdong Liu School of Automatic Beijing Institute of Technology Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology Beijing China

Due to the non-volatile property, the molybdenum disulfide solid lubrication is widely used in various bearings of spacecraft. Spot flaking of the lubricating material appearing at the end of life will cause the deterioration of friction characteristic and degrade the positioning performance of high precision satellite servo system. To solve this problem, the control model of the servo system based on permanent magnet synchronous machine (PMSM) including friction model is built, and the influence on servo system performance with variation of friction characteristic is investigated in this paper. And then, an active damping control strategy is proposed to improve the precision and speed of response of servo system in the case of deteriorating friction characteristic, and compared with the conventional PI control strategy. Finally, the effectiveness of the active damping control strategy is verified by the simulations and the experimental results on the PMSM driver of the DQ-1 satellite payload.

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Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

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arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Data-driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Research on Path Planning of Mobile Robot Based on DWA-IMP-A* Algorithm

Research on Path Planning of Mobile Robot Based on DWA-IMP-A...

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Chinese control Conference (CCC)

作者： Zhi Liu Lin Zhang Shoukun Wang Tianwei Niu Yongkang Xu Jin Cao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A* algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A* combined with reverse path and dynamic window method (DWA-IMP-A*) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A* algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles.

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Unknown Fault Diagnosis Based on Transfer Learning Under Multiple Working Conditions

Unknown Fault Diagnosis Based on Transfer Learning Under Mul...

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Chinese Automation Congress (CAC)

作者： Jingyan Huang Liling Ma Junzheng Wang School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China

The rapid development of modern industry makes fault diagnosis more important. In engineering practice, equipment often works under different working conditions, and there may be several new faults, called unknown faults, which cannot be identified by the traditional intelligent fault diagnosis method. To solve the difficulty of unknown faults diagnosis under multiple working conditions, a fault diagnosis method based on adversarial transfer learning and multitask learning is proposed in this paper. First, known faults and unknown faults are distinguished by adversarial transfer method with sample weights. Second, the fault classification module and fault location module trained by multitask learning are used to classify and locate the known faults. Besides, the number of categories of unknown faults is identified by the Bisecting K-Means clustering algorithm with silhouette coefficient. Experiment shows that the proposed method can be well applied to unknown fault diagnosis, and has a better recognition rate than other comparison methods.

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Event-Triggered Pseudo Supervised Meta Learning for Susceptibility Assessment of Acute Mountain Sickness

Event-Triggered Pseudo Supervised Meta Learning for Suscepti...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Lei Wang Dawei Shi Lingling Zhu Junzheng Wang School of Medical Technology Beijing Institute of Technology Beijing P. R. China MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing P. R. China Beijing Institute of Basic Medical Sciences Beijing P. R. China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Acute altitude sickness presents as a syndrome characterized by individuals’ incapacity to adapt to the hypoxic conditions of high-altitude environments. The assessment of this sickness typically relies on the subjective evaluation of symptom severity using the Lake Louise score, introducing potential subjective biases and rendering the labels unreliable. To address this challenge, this paper introduces a weakly supervised meta-learning algorithm tailored for the assessment of susceptibility to acute altitude sickness within a few-shot framework. Initially, objective sleep data is subjected to clustering, and pseudo-labels are generated from the cluster result exhibiting the highest mutual information with the Lake Louise score. This approach capitalizes on the mutual complementarity of subjective and objective data, thereby enhancing label credibility. Subsequently, a task-driven pseudo-label supervised meta-learning algorithm is formulated to comprehensively explore individual differences. Moreover, recognizing the necessity for model updates during online learning, an event-triggered online learning algorithm is devised to optimize computational resources. Experimental validation utilizing data from 18 real subjects demonstrates promising results: an accuracy of 79.900% and an F1 score of 79.342% for acute altitude sickness susceptibility assessment, with further improvements evident through the online updated model’s accuracy of 81.992% and F1 score of 81.366% on the test set. These outcomes underscore the viability of the proposed event-triggered weakly supervised meta-learning algorithm in the assessment of susceptibility to acute altitude sickness.

关键词： Metalearning Accuracy Computational modeling Supervised learning Clustering algorithms Lakes Rendering (computer graphics)

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