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Nonlinear time-varying systems identification based on multi-dimensional taylor network and variable forgetting factor recursive least squares algorithm

Nonlinear time-varying systems identification based on multi...

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1st IFAC Conference on Modelling, Identification and control of Nonlinear systems, MICNON 2015

作者： Zhang, Jiao-Jun Yan, Hong-Sen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

A novel method for nonlinear time-varying systems identification based on multi-dimensional Taylor network and variable forgetting factor recursive least squares algorithm is proposed. In this paper, the connection weight coefficients of multi-dimensional Taylor network are regarded as time-varying parameters, which are trained by the variable forgetting factor recursive least squares algorithm, to reflect the input-output change of nonlinear time-varying systems. Simulation results show that the method proposed in this paper is valid.

关键词： Time varying networks

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Non-Smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer 42

Non-Smooth Speed Control of Switched Reluctance Motor Based ...

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42nd Chinese control Conference, CCC 2023

作者： Yin, Qingzhao Tian, Zheng Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789887581543

This paper investigates the speed regulation control of switched reluctance motor (SRM) systems. To improve the antidisturbance performance of SRM, a composite non-smooth control strategy is proposed. First, the structure of SRM is analyzed, and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance. A virtual control function is introduced to represent the nonlinear part of the torque equation, whose inverse function is cascaded to linearized the nonlinear model. Second, a generalized proportional integral observer (GPIO) is constructed to estimate the lumped disturbance of the system, which is used for feedforward compensation design. Finally, a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO (FTP+GPIO). The speed error closed-loop system can be regarded as a first-order finite time control system with bounded disturbances. Strict analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop system. The effectiveness of the proposed method is verified by simulation results. Moreover, it is compared with proportional feedback combining feed-forward compensation (P+GPIO) method and proportional integral (PI) control method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Closed loop systems

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YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario 10

YOLO-MSP: Effective Cigarette Detection YOLO Network for Sur...

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10th International Conference on systems and Informatics, ICSAI 2024

作者： Zhang, Zhiwei Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

ISBN: (纸本)9798331510138

This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We introduce a Multi-Spatial Pooling (MSP) bottleneck mechanism integrated into a YOLOv8 backbone, which significantly improves the network's ability to discern smaller objects without compromising real-time inference capabilities. This enhancement facilitates the extraction of relevant features across various scales and efficiently handles the computational overhead, thereby boosting the detection accuracy and operational efficiency. Our results demonstrate a substantial improvement in performance metrics over traditional and existing neural network approaches, making this method highly suitable for real-time applications in diverse scenarios. © 2024 IEEE.

关键词： Deep learning

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PMSM System Identification by Knowledge-informed Machine Learning 22

PMSM System Identification by Knowledge-informed Machine Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yu, Yue Tong, Jiageng Xia, Jinhui Su, Jinya Li, Shihua School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9798331527471

Because of its excellent efficiency, compact dimensions, and accurate control features, Permanent Magnet Synchronous Motor (PMSM) are experiencing widespread applications across various industries. By accurately characterizing the dynamic behavior of PMSM systems through system identification, engineers can ensure that PMSM motors reach their maximum potentials and meet the stringent requirements of modern industrial and technical systems while reducing energy consumption and maintenance costs. The traditional recursive least squares method is sensitive to noises, and unable to accurately identify parameters in complex environments. Pure data-driven models lack interpretability and require complex model architecture and computational costs. To this end, this work draws knowledge-informed neural ordinary differential equations (NODEs) for system identification, which embeds system prior knowledge into the NODEs for more efficient and accurate model learning. Comparative simulations show that this method not only obtains a higher-precision system model, but also significantly reduces the amount of training data and computation costs. © 2024 IEEE.

关键词： Cost reduction

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Motion control and Motion Coordination of Bionic Robotic Fish： A Review

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Journal of Bionic engineering 2018年第4期15卷 579-598页

作者： Junzhi Yu Ming Wang Huifang Dong Yanlu Zhang Zhengxing Wu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Information and Electrical Engineering Shandong Jianzhu University Jinan 250101 China

Fish＇s outstanding motion and coordination performance make it an excellent source of inspiration for scientists and engineers aiming to design and control next-generation autonomous underwater vehicles within the framework of bionics. This paper offers a general review of the current status of bionic robotic fish, with particular emphasis on the hydrodynamic modeling and testing, kinematic modeling and control, learning and optimization, as well as motion coordination control. Among these aspects, representative studies based on ideas and concepts inspired from fish motion and coordination are discussed. At last, the major challenges and the future research directions are addressed in the context of integration of various research streams from ichthyologic, hydrodynamic, mechanical, electronic, control, and artificial intelligence. Further development of bionic robotic fish can be utilized to execute some specific missions in complex underwater environments, where operations are unsafe or impractical for divers or conventional underwater vehicles.

关键词： bionic robotic fish motion control coordination control fish swimming learning and optimization

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Position Tracking control for PMSM System Based on Continuous Sliding Mode control and Sliding Mode Observer 41

Position Tracking Control for PMSM System Based on Continuou...

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第41届中国控制会议

作者： Qian Wan Huiming Wang Xia Sun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing Institute of Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this article,a robust finite-time control strategy using continuous terminal sliding mode control(CTSMC) and sliding mode observer(SMO) is proposed to solve the problem of position tracking of permanent magnet synchronous motor(PMSM) servo *** control performance of such a system is always affected by internal and external *** this reason,a continuous sliding mode control(CSMC) method with fast convergence and strong anti-disturbance is firstly ***,considering that the disturbance of the actual system is generally in various forms and time-varying,to further improve the disturbance rejection ability of the system,an SMO based CTSMC method is proposed,where the SMO is devised to estimate time-varying disturbances and the estimations are employed to the position controller through feedforward compensation for disturbance *** present control method can not only improve the tracking rate,but also enhance the disturbance suppression ability of the *** with the PD control,CSMC,and CTSMC,simulation results demonstrate that the proposed control method can achieve the promised control performance for the studied system.

关键词： Continuous terminal sliding mode(CTSM) sliding mode observer(SMO) position control permanent magnet synchronous motor(PMSM)

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Dynamics parameter identification of industrial robots based on immune algorithm

Dynamics parameter identification of industrial robots based...

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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Jianjun Gan, Yahui Dai, Xianzhong Ministry of Education School of Automation Southeast University Key Laboratorty of Measurement and Control of Complex Systems of Engineering Nanjing China

ISBN: (纸本)9781665465335

Dynamics parameter identification is a key factor and a difficult area in the development of robot motion control technology. In order to obtain accurate dynamics parameters, an integral identification method combined with the immune algorithm is studied and experimented with. Firstly, the dynamics model of the industrial robot is derived from the Newton-Euler iteration method. Then, taking the condition number of the observation matrix as the optimization index, an ideal excitation trajectory is optimized by the immune algorithm. Butterworth zero-phase low-pass filter and the weighted least square method are used to solve the minimum parameter set of dynamics. A new validation trajectory is designed using the fmincon toolbox to cross-validate the identified dynamics model. Finally, the above identification algorithm is tested with Inexbot LG-607L robot in China as the experimental object. The experimental results show that the immune algorithm can calculate the excitation trajectory with a lower matrix condition number than the fmincon method and the genetic algorithm. And the parameter identification effect of the integral method is accurate. © 2022 IEEE.

关键词： Trajectories

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Development of electric cart for improving walking ability — application of control theory to assistive technology

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Science China(Information Sciences) 2017年第12期60卷 262-270页

作者： Jinhua SHE Yasuhiro OHYAMA Min WU Hiroshi HASHIMOTO School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Master Program of Innovation for Design & Engineering Advanced Institute of Industrial Technology

This paper explains the development of an electric cart that helps the elderly maintain or improve their physical strength. Unlike commercially available ones, it has a pedal unit that provides some exercise for a user in training his lower limbs. An impedance model describes the feeling of pushing the pedals. The largest pedal load is determined based on a pedaling experiment. An H∞ controller is designed for each of the largest pedal load and virtually no load. A control law, which is based on the concept of dynamic parallel distributed compensation, is designed using the rating of perceived exertion of a driver as a criterion to choose a pedal load between the largest and almost zero. Five university students and twelve elderly people participated experiments to verify the system design and the validity of the system.

关键词： aging Borg’s scale dynamic parallel distributed compensation electrical cart H_∞ control Karvonen formula lower limbs pedaling rating of perceived exertion

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Game-theoretical explorations of the mesoscale flow structure and regime transitions in bubble columns

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Particuology 2020年第1期18卷 100-108页

作者： Shuo Yuan Yifen Mu Xiaoping Guan Zhixin Liu Jianhua Chen Ning Yang Lin Zhang Key Lab of Systems and Control Institute of Systems SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Understanding the mesoscale structure and regime transition in bubble columns is of great significance for reactor design and *** on the energy-minimization multiscale(EMMS)model,a noncooperative game model with constraints is proposed to investigate the structural properties of gas-liquid systems in which small and large bubbles are chosen as players and the energy consumption form the objective *** conservation equations of the system can be regarded as the constraints of the *** the formulated noncooperative game model,the concept of the generalized Nash equilibrium(GNE)is used to characterize the *** algorithm is developed to numerically compute the GNE and some important structural parameters in the *** numerical results show the existence of the GNE for all values of the superficial gas velocity *** Ug varies,the trends in the state variables can be observed and the critical point of Ug *** overall trend of the flow regime transition agrees with the original EMMS model and experimental results,although the GNE calculation also reveals different single-bubble dominant mechanisms with increasing Ug.

关键词： Bubble column Mesoscale Regime transition Game theory Generalized Nash equilibrium EMMS model

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