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Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-driven Predictive control

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IEEE Transactions on Automatic control 2024年第6期70卷 3920-3935页

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

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a pre-specified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies. © 1963-2012 IEEE.

关键词： Predictive control systems

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

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

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

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 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. © 2024 IEEE.

关键词： Scheduling algorithms

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Active Damping control Used for servo system of Satellite Payload with Deteriorating Friction 26

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

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26th International Conference on Electrical Machines and systems, ICEMS 2023

作者： Li, Jialin Chen, Zhen Hu, Hengzai Liu, Xiangdong 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

ISBN: (纸本)9798350317589

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. © 2023 IEEE.

关键词： Friction

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Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm 2

Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorit...

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2022 2nd International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2022

作者： Wang, Sheng Ma, Liling Wang, Junzheng Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China

The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sampling technique (SMOTE). For the sampler, an improved synthetic minority over-sampling technique based on the characteristics of neighborhood distribution (CND-SMOTE) algorithm is used to equilibrate the minority class samples and the majority class samples. For the classifier, the parameter optimization method of support vector machines based on the bat algorithm (BA-SVM) is used to solve the multi-classification problem of faulty samples. Finally, experimental results prove that the CND-SMOTE+BA-SVM algorithm can synthesize high-quality minority fault samples, increase the classification accuracy rate of fault samples, and decrease the time spent on the classification. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Event-triggered learning of Euler-Lagrange systems: A Koopman operator framework 63

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

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zheng, Kaikai Shi, Dawei Li, Shilei Shi, Yang Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Victoria Faculty of Engineering Department of Mechanical Engineering VictoriaBCV8N 3P6 Canada

ISBN: (纸本)9798350316339

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. © 2024 IEEE.

关键词： Lagrange multipliers

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Wu, Wenjing Fu, Hanjing Cui, Kaixin Liu, Zhigang Yang, Dong Shi, Dawei Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Data limitation caused by the difficulty of data acquisition, the high cost of data collection, and the changes of working conditions is a serious obstacle to the accurate lithium-ion battery SOH estimation for spacecrafts system. To achieve accurate few-shot lithium-ion battery state of health(SOH) estimation and enhance the adaptability of learning methods across varying working conditions, a domain similarity model agnostic meta-learning method (DS-MAML) is proposed. First, we design a dynamic-static feature extraction method to adequately exploit information from the limited battery data and link the obtained features by gray correlation coefficients to collect sufficient information. Then, by calculating the domain similarity between the training tasks through the maximum mean discrepancy algorithm, the training tasks are ranked to reduce the re-training time. Finally, an LSTM model is added to the meta-learning framework to capture the long-term dependency relationships between SOH and time series of the voltage, and the ranked tasks are embedded in the meta-training process to improve adaptability in different working conditions. The effectiveness of the proposed method is validated by NASA and MIT datasets, and comparative experimental results illustrate that the proposed method average error is reduced by 73%, and the running speed is increased by 20% compared with traditional MAML in few-shot scenarios. © 1965-2011 IEEE.

关键词： State of charge

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Adaptive Vibration Reduction control Strategy for 3-DOF Parallel Energy-Efficient Stabilization Robotic systems

Adaptive Vibration Reduction Control Strategy for 3-DOF Para...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Shaokun Yang Junzheng Wang Wei Shen Dongchen Liu Qianye Lin Key Laboratory of Servo Motion System Drive and Control Beijing Institute of Technology Beijing China

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The parallel energy-efficient stabilization robotic systems are well-suited for large-load vibration reduction and stabilization operations. However, challenges arise, including uncertainties in environmental parameters, nonlinear disturbances in pneumatic structures, and limitations in dynamic vibration reduction performance during vibration control. We propose a reference trajectory compensation approach based on dynamic pneumatic cylinder stiffness compensation and environmental parameter estimation to tackle the tracking force error problem in vibration reduction control. Additionally, dynamic adjustments are made through an adaptive damping parameter adjuster to enhance the dynamic performance of vibration reduction. The results indicate that the proposed vibration reduction control strategy effectively resolves the tracking force problem and achieves superior outcomes compared to the basic impedance control strategy in vibration reduction control.

关键词： Vibrations Uncertainty Parameter estimation Dynamics Force Pneumatic systems Energy efficiency

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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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2023 China Automation Congress, CAC 2023

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

ISBN: (纸本)9798350303759

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. © 2023 IEEE.

关键词： Failure analysis

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Improved Model Predictive Speed control of PMSMs Based on Delay Compensation and Fuzzy Tuning

Improved Model Predictive Speed Control of PMSMs Based on De...

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Chinese control and Decision Conference, CCDC

作者： Chuanyu Ge Wei Shen Junzheng Wang Tao Wang School of Automation Key Laboratory of Servo Motion System Drive and Control Beijing Institute of Technology Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

MPSC of PMSMs can give the optimal reference current signal but fails to get enough attention in the industry, because the application of MPSC depends on the accurate speed loop model and the control parameters in the controller are usually selected by trial-and-error method. The issues above are solved by following steps. Firstly, a fuzzy weight factor tuning method is proposed to adjust the coefficients online in both reference trajectory and cost function. This method remarkably improves the transient response and reduce the steady speed fluctuations. Secondly, a two-step predictive strategy is adopted to reduce the overshoot brought by the above method and time delay of the control system. Lastly, a new MPSC with shorter rise time, smaller overshoot and steady errors is proposed. Experimental results validate the superior performance of the improved MPSC than the conventional MPSC.

关键词： Industries Transient response Fluctuations Delay effects Velocity control Predictive models Cost function

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

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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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