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2023 China Automation Congress, CAC 2023

作者： Liu, Ruohan Ren, Chunping Li, Degen Liu, Yongli 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 Heilongjiang University of Science and Technology Academic Theory Research Department Harbin China School of Mining Engineering Heilongjiang University of Science and Technology Harbin China

ISBN: (纸本)9798350303759

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. © 2023 IEEE.

关键词： Three term control systems

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Sensor fault detection under asynchronous switched systems with state delays

Sensor fault detection under asynchronous switched systems w...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Ali, Shafqat Jiang, Yuchen Luo, Hao Raza, Muhammad Taskeen Shahid, Faizan Faisal, Shah Harbin Institute of Technology Control and Simulation Center Harbin150001 China Lahore College for Women University Department of Electrical Engineering Lahore54000 Pakistan

ISBN: (纸本)9798350337754

This study aims to develop a technique for detecting sensor faults in continuous-time asynchronous switched time- delay systems using Mode Dependent Average Dwell Time (MDADT) switching. Introducing a sliding mode observer for fault detection aims to generate a residual signal. In complex asynchronous switched systems, the observer lags behind the corresponding subsystem or mode, resulting in matched and mismatched periods. Furthermore, the presence of time delays in asynchronous switched systems, such as delays between subsystems and observer lag time, and state delays, adds to the complexity of the problem. A Lyapunov function is chosen for matched and mismatched periods combined with MDADT to achieve global uniform asymptotic stability. By using the H∞ performance index, the presence of faults mitigates the influence of residual error signals. By establishing adequate conditions, linear matrix inequalities can be derived, and observer parameters and matrix parameters can be designed accordingly. In conclusion, an illustrative numerical example demonstrates the efficacy of the proposed technique. © 2023 IEEE.

关键词： Time delay

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Facial Expression Recognition via Closed-Loop Transcription

Facial Expression Recognition via Closed-Loop Transcription

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Annual Conference of Industrial Electronics Society

作者： Xuan Liu Jiachen Ma Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin PR China Department of Control Science and Engineering Harbin Institute of Technology Weihai PR China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Convolutional Neural Networks (CNNs) are the most widely used and successful approaches for accomplishing the Facial Expression Recognition (FER) task. However, these technologies appear to have reached a bottleneck since the networks are becoming increasingly complex and heavier, yet the performance improvements remain minimal. The "black box" nature of CNNs makes it challenging to identify and address this crux. This paper aims to address the issue by utilizing the principles of data compression and discriminative representation within a plausible theoretical framework. We are employing an innovative computational framework, drawing inspiration from feedback loop control systems, to develop an interpretable closed-loop transcription model to generate a Linear Discriminative Representation (LDR) for multi-class real-world datasets. This model, comprised of an encoder and decoder that facilitates data mapping between the data space and the feature space, employs the output of the transcription system as feedback or guidance, with the objective of improving the accuracy and efficiency of the transcription process. The optimal LDR is learned jointly for all data classes through a two-player minimax game between the encoder and decoder for the learned representation over a single rate reduction-based objective. The studies conducted on the FER2013 dataset show promising results for the proposed closed-loop formulation. The visual quality of the learned decoder and the classification performance of the encoder are comparable to those of large and complex networks. An accuracy of 70.97% was attained on the FER2013 dataset by applying Principal Component Analysis (PCA) to the learned representation.

关键词： Industrial electronics Visualization Feedback loop Accuracy Face recognition Computational modeling Games Aerospace electronics Decoding Principal component analysis

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Improved adaptive-gain super-twisting sliding mode control of transmission line inspection UAVs

Improved adaptive-gain super-twisting sliding mode control o...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Kunpeng Kang, Zhuang Gu, Lei Li, Wenlong Shen, Xiaoning Gao, Yabin Yichun Power Supply Company State Grid Heilongjiang Electric Power Company Limited Yichun153000 China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9798350368604

This paper presents an improved adaptive-gain super-twisting sliding mode control method for transmission line inspection unmanned aerial vehicles (UAVs) to track desired trajectories in the presence of external disturbances. A cascaded control structure is designed to compute the desired accelerations using the proposed controller, which are then utilized to determine the desired orientation and angular rates. By dynamically adjusting the gains in the controllers, the chattering problem is effectively suppressed, and the system’s transient performance and robustness are significantly enhanced. Furthermore, using Lyapunov function theory, the finite-time stability of the system is rigorously proven. Finally, simulation studies demonstrate the effectiveness and superior performance of the proposed method. © 2024 IEEE.

关键词： Lyapunov functions

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Multi-Robot Real-time Game Strategy Learning based on Deep Reinforcement Learning

Multi-Robot Real-time Game Strategy Learning based on Deep R...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Deng, Ki Li, Yanjie Lu, Songshuo Mu, Yongjin Pang, Xizheng Liu, Qi Harbin Institute of Technology Shenzhen Department of Control Science and Engineering Shenzhen China

ISBN: (纸本)9781665481090

With the booming development of artificial in-telligence, the autonomous decision-making of robots on the battlefield has become a future trend. In the field of en-tertainment games, some institutions have already attained outstanding results in multi-agent decision-making tasks by using reinforcement learning algorithms. But in the field of robot confrontation game, the robot confrontation strategies are still based on the simple logic of artificial design in most cases, which hinders the further improvement of robot strategies. In this paper, a reinforcement-learning-based multi-robot control method is proposed to improve the strategy performance. We enhanced the reinforcement learning algorithm PPO with the self-play method by easy-to-implement tricks for multi-agent training and improve training efficiency. In simulated experiments based on the rules of the RoboMaster University AI Challenge, we show our method has a much higher winning percentage than traditional approaches like behavior tree. © 2022 IEEE.

关键词： Reinforcement learning

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Quo vadis artificial intelligence?

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Discover Artificial Intelligence 2022年第1期2卷 1-19页

作者： Jiang, Yuchen Li, Xiang Luo, Hao Yin, Shen Kaynak, Okyay Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China Department of Mechanical and Industrial Engineering Faculty of Engineering Norwegian University of Science and Technology Trondheim7033 Norway Department of Electrical and Electronics Engineering Bogazici University Istanbul34342 Turkey

The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work;they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run. © The Author(s) 2022.

关键词： Artificial intelligence

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Rail Fatigue Crack Classification Based on Improved DPC Algorithm Using Acoustic Emission technology

Rail Fatigue Crack Classification Based on Improved DPC Algo...

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第43届中国控制会议

作者： Qinghua Song Xin Zhang Jiazhong Cui Yongqi Chang Shuzhi Song Yi Shen Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The effective classification of crack,as a key aspect in maintaining rail safety,plays a significant role in ensuring the reliable operation of high-speed *** are mainly supervised and unsupervised algorithms for classification *** learning algorithms require a large amount of labeled data and have limited generalization capability,and unsupervised learning algorithms have low classification *** address these shortcomings,a multi-center density peak clustering based on the multi-layered weight density(MDPC-MWD) is proposed to classify rail cracks more efficiently using acoustic emission *** information of different rail cracks is comprehensively reflected by studying the entropy feature of the signals from the perspective of singular ***,an improved multi-layered weight density is proposed to address the uneven density distribution of the crack ***,the classification results are obtained through micro-cluster self-recognition and self-merging *** method is demonstrated in the rail fatigue experiments,and the results show that the proposed MDPC-MWD method achieve outstanding classification performance.

关键词： Acoustic Emission Rail Crack Classification Density Peak Clustering Uneven Density Distribution Multiple Peaks

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Design and Implementation of Steering Torque Feedback System for Steer-by-Wire Vehicles

Design and Implementation of Steering Torque Feedback System...

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第43届中国控制会议

作者： Chao Ma Linhui Zhao Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Steer-by-wire(SBW) is an advanced technology of great significance for the intelligent *** implement the function of the road feeling feedback for SBW,this paper presents a design of the road feeling feedback system,including the mechanical structure and the control *** target feedback torque is planned by the method of function fitting,and the controller is designed to track the target torque by combining the feedback control and the feedforward *** paper designs an experimental bench and validates the performance of the system by hardware in-loop(HIL) *** results show that the system can provide an approximate feedback torque and a comfortable driving experience.

关键词： Steer-by-wire Motor control Hardware in-loop experiment Road feeling feedback

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Robust Event-triggered Collision-avoidance Formation control for Uncertain Euler-Lagrange Systems

Robust Event-triggered Collision-avoidance Formation Control...

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第43届中国控制会议

作者： Chuanjiang Li Xinxiao Liu Yuanyuan Zhu Yanning Guo Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates an event-triggered formation with collision-avoidance problem for uncertain Euler-Lagrange systems with external disturbances. The designed controller can be implemented in directed networks relied on relative information. First,potential functions-based to prevent the collision between agents. Second,by using the adaptive law technique to address the unknown external disturbances and nonlinear dynamics,the estimation for uncertain terms is used in the controller design. In addition,an event-triggered strategy is proposed to reasonably use the limited communication resources and Zeno behavior is excluded. Furthermore,the proposed control algorithm is explored to make all agents achieve the desired formation configuration. Finally,an example is showcased to illustrate the effectiveness of the proposed scheme.

关键词： Uncertain Euler-Lagrange systems Formation control Collision avoidance Event-triggered control

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Optimal Pole-Transition Current Distribution for Constant Torque Maintenance and Stator Copper Loss Minimization in Variable-Pole Machines

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IEEE Transactions on Transportation Electrification 2025年

作者： Lin, Xinpo Wu, Yixuan Olson, Gustaf Falk Liu, Jianxing Wu, Ligang Peretti, Luca Department of Control Science and Engineering Harbin Institute of Technology Weihai264200 China Division of Electric Power and Energy Systems Kth Royal Institute of Technology Stockholm10044 Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

Variable phase-pole machines possess the capability to expand their operational range by employing magnetic pole change while adhering to the same electrical voltage and current limits. This paper proposes a strategy to achieve a pole change with constant torque while simultaneously optimizing current distributions for stator copper loss minimization. The overall outcome is the ability to facilitate smooth dq0 current transitions while reducing the energy consumption of variable phase-pole machine drives. Experimental results affirm the efficacy of this approach during the pole change process. © 2025 IEEE.

关键词： Electric machine control

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