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Coordinated Adaptive Impedance control of Redundantly Actuated Parallel Manipulators

IEEE TRANSACTIONS ON HUMAN-MACHINE systemS 2025年

作者： Ma, Nan Cheneler, David He, Guangping Yuan, Junjie Bian, Gui-Bin Northwestern Polytech Univ Sch Mech & Elect Engn Xian 710072 Peoples R China Univ Lancaster Sch Engn Lancaster LA1 4YW England North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Redundantly actuated parallel mechanisms (RAPMs) have been widely adopted in advanced robotic systems and precision machine tools. It has been demonstrated that redundant actuation can improve the performance of mechatronic systems but introduce challenges with respect to control. One main difficulty is in establishing an accurate dynamic model of the RAPM system. With an inaccurate dynamic model, the torque applied by the actuators will be incorrect, leading to increased antagonistic forces in the system. To solve this problem, a novel coordinated adaptive impedance control approach based on a new adaptive impedance control law is presented here, along with proof of the stability of the closed-loop system. The control algorithm has been validated experimentally by a prototype cable-driven parallel manipulator. It can be seen from the experimental results that the proposed control method is an effective way to correct the antagonistic forces of the system, thus facilitating the improvement of its dynamic performance and its efficacy in different applications.

关键词： Impedance Robots Adaptation models Robot kinematics Actuators Stability analysis Kinematics Wires Steel Manipulator dynamics Actuation redundancy adaptive impedance control coordination control parallel manipulators uncertain model

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LLM-Driven Cognitive Modeling for Personalized Travel Generation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2025年

作者： Ge, Shichao Ye, Peijun Zhang, Renrui Zhou, Min Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automation & Intelligence Beijing 100044 Peoples R China Chinese Acad Sci Inst Automation Beijing 100190 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automation Qingdao 266590 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month's data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.

关键词： Computational modeling Decision making Accuracy Planning Automation Computer architecture Traffic control Memory modules Cognition Adaptation models Agent-based model (ABM) cognitive model large language model (LLM) travel behavior

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Event-Triggered Sliding-Mode control for a Discrete-Time Muscle-Driven Musculoskeletal system

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 5866-5880页

作者： Fan, Yerui Wu, Yaxiong Yuan, Jianbo Gao, Jie Qiao, Hong Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China

Bionic muscle-driven musculoskeletal systems can dynamically adjust the stiffness between active and antagonistic muscles to improve stability. However, they retain many problems, such as difficulty with sensory feedback control, which arises from the strong coupling of models and large computational loads encountered when optimizing the muscle force online. In this study, based on a bionic muscle model, an event-triggered-sliding-mode controller for a discrete-time-muscle-driven musculoskeletal system (MDMS) was designed to drive the system into a bounded region. Specifically, based on the Hamilton principle and discretization of muscle contraction dynamics, a new discrete-time-muscle-driven musculoskeletal model was constructed to facilitate the decoupling of the muscle model, feedback of the muscle state, and improvement of model control accuracy. Second, to guarantee the boundedness of the closed-loop system, an even-triggered-sliding-mode control law was established by introducing the input-to-state stable (ISS) method to a new type of discrete-time MDMS. The design ensured the convergence of different triggering cases, and an event-triggered-sliding-mode control law was established to obtain faster and smoother response characteristics. Finally, stability was ensured using the Lyapunov synthesis principle. The experimental results demonstrated that the proposed controller could effectively reduce the computational load while maintaining the same performance using a time-based approach. Note to Practitioners-The motivation of this study is to delve into the utilization of the Lyapunov control theory to enhance the control performance of the bionic muscle model. Additionally, it aimed to explore the initial application of the event-trigger mechanism within the musculoskeletal system. The primary focus is on discrete musculoskeletal system modeling, which facilitates the achievement of sensory feedback control for nonlinear muscle models characterized by strong c

关键词： Musculoskeletal system bio-inspired muscle model discrete-time system event-triggered control sliding mode controller

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A Comparative Study of Machine Learning Models for Parkinson's Disease Detection

A Comparative Study of Machine Learning Models for Parkinson...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Bunterngchit, Chayut Bunterngchit, Yuthachai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Siam Univ Grad Sch Engn Bangkok Thailand

ISBN: (纸本)9781665495011

Parkinson's disease is a major public health concern, affecting over 6 million people worldwide. The objective of this paper is to assist doctors and clinicians in accurately detecting the disease at an early stage. Previous research proposed various models that gave very high accuracy. However, very few of them examined the processing time of each model, which is an important consideration in decision making. The most common method for diagnosing this disease is through voice signal recordings. This paper formulates 10 machine learning-based predictive models on a biomedical voice measurement dataset. A genetic algorithm is applied as a feature selection algorithm. The highest prediction accuracy after running 10 generations is 97.96%. The features of the most accurate model are reduced from 22 to 9 features. The processing time of the most accurate model is 1.83 seconds. The best improvement in accuracy after applying this feature selection algorithm is 16.33%.

关键词： Parkinson's disease genetic algorithm machine learning feature selection

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Containment control with input and velocity constraints

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AUTOMATICA 2022年 142卷

作者： Xu, Jiahao Lin, Peng Cheng, Long Dong, Hairong Cent South Univ Sch Automat Changsha 410083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In this paper, containment control for double-integrator discrete-time multi-agent systems is investigated with switching graphs, nonconvex input constraints and nonconvex velocity constraints. In order to eliminate the nonlinearities caused by the nonconvex input and velocity constraints, we first introduce two time-varying scaling factors to transform the original constrained system into an unconstrained time-varying linear one. By proposing an inductive method based on the lower bounds of the scaling factors, we decouple the relevance of the two kinds of constraints and transform the obtained time-varying system into a new equivalent system. It is shown that all agents converge into the convex hull of the leaders even when switching graphs, nonconvex control input constraints and nonconvex velocity constraints coexist, as long as there exists at least one directed path from one of the leaders to each agent in the union of the communication graphs. Finally, a simulation example is given to illustrate the obtained theoretical results. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Containment control Nonconvex constraints Switching graphs

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Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2023年第7期37卷 1555-1581页

作者： Zhao, Tianhong Huang, Zhengdong Tu, Wei Biljecki, Filip Chen, Long Shenzhen Univ Sch Architecture & Urban Planning Shenzhen Peoples R China Natl Univ Singapore Dept Architecture Singapore Singapore Natl Univ Singapore Dept Real Estate Singapore Singapore Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus's spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model's performance.

关键词： Graph deep learning multiview learning travel demand prediction multimodal transportation smart card data

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4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4606-4620页

作者： Fan, Lili Wang, Junhao Chang, Yuanmeng Li, Yuke Wang, Yutong Cao, Dongpu Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Waytous Co Ltd Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China

The rapid development of autonomous driving technology has driven continuous innovation in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key sensing devices. Leveraging its all-weather operational characteristics and robust perception capabilities in challenging environments, 4D mmWave radar plays a crucial role in achieving highly automated driving. This review systematically summarizes the latest advancements and key applications of 4D mmWave radar in the field of autonomous driving. To begin with, we introduce the fundamental principles and technical features of 4D mmWave radar, delving into its comprehensive perception capabilities across distance, speed, angle, and time dimensions. Subsequently, we provide a detailed analysis of the performance advantages of 4D mmWave radar compared to other sensors in complex environments. We then discuss the latest developments in target detection and tracking using 4D mmWave radar, along with existing datasets in this domain. Finally, we explore the current technological challenges and future directions. This review offers researchers and engineers a comprehensive understanding of the cutting-edge technology and future development directions of 4D mmWave radar in the context of autonomous driving perception.

关键词： Radar Radar antennas Millimeter wave communication Microstrip antennas Antenna arrays Radar detection Autonomous vehicles 4D mmWave radar autonomous driving perception multi-modal fusion 3D object detection

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Toward Generalizable Robotic Dual-Arm Flipping Manipulation

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第5期71卷 4954-4962页

作者： Huang, Haifeng Zeng, Chao Cheng, Long Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China South China Univ Technol Coll Automat Sci & Engn GuangDong Engn Technol Res Ctr Control Intelligen Guangzhou 510640 Peoples R China Univ Hamburg Dept Informat Tech Aspects Multimodal Syst TAMS Grp D-22527 Hamburg Germany Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Robotic dual-arm manipulation often requires close cooperation between the arms. Dual-arm manipulation tasks are always difficult to program in advance, and then, executed autonomously by the robots. Learning from demonstration is an efficient programming method for robots that can transfer human skills to robots. However, conventional skill learning methods, e.g., dynamic movement primitives (DMPs) can only characterize the motion information of each dimension independently, and cannot take into account the relationship between multidimensional information. Participially, flipping manipulation is quite common in industry production lines, but it has not been well addressed yet to provide a robotics solution to this task. In this article, we propose an improved DMP model, called the object-level constrained DMP, which effectively preserves the association between multidimensional information. Similarly, we also propose an orientation generalization method for the flipping task. In addition, we show how to demonstrate the flipping task via a teleoperation system. Finally, experiments are performed on a Baxter robot to verify the effectiveness of the methods.

关键词： Dynamic movement primitive (DMP) flipping task learning from demonstration (LfD) skill generalization

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Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 164卷

作者： Wu, Jingda Huang, Chao Huang, Hailong Lv, Chen Wang, Yuntong Wang, Fei-Yue Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) emerges as a pivotal tool in crafting these BP strategies. This paper offers a comprehensive review of RL-based BP strategies, spotlighting advancements from 2021 to 2023. We completely organize and distill the relevant literature, emphasizing paradigm shifts in RL-based BP. Introducing a novel categorization, we trace the trajectory of efforts aimed at surmounting practical challenges encountered by autonomous vehicles through innovative RL techniques. To guide readers, we furnish a quantitative analysis that maps the volume and diversity of recent RL configurations, elucidating prevailing trends. Additionally, we delve into the imminent challenges and potential directions for the future of RL-driven BP in AD. These directions encompass addressing safety vulnerabilities, fostering continual learning capabilities, enhancing data efficiency, championing collaborative vehicular cloud networks, integrating large language models, and enhancing ethical considerations.

关键词： Autonomous driving Reinforcement learning Behavior planning Decision Autonomous vehicle

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Modeling Digital Personality: A Fuzzy-Logic-Based Myers-Briggs Type Indicator for Fine-Grained Analytics of Digital Human

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第1期11卷 1096-1107页

作者： Wang, Tan Ye, Peijun Lv, Hongqiang Gong, Weichao Lu, Hao Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Digital human in cyberspace can help provide humanized services in specific applications, such as question & answer systems, recommender systems, chatter robots, and intelligent assistants. While most researches focus on behavior analytics, few of them integrate the personality that is also a closely related factor. As a classic indicator for personality representation, Myers-Briggs type indicator (MBTI) categorizes an individual into mutually exclusive types from four dichotomous axes (extraversion versus introversion, sensing versus intuition, thinking versus feeling, judging versus perceiving). Traditional recognition method using MBTI simply measures the user's preference frequency in each axis through questionnaires, treating the dominant value as the identified result. Such a paradigm, however, represents all the people with only 16 types and cannot distinguish heterogeneous users clearly. This article proposes a novel personality recognition method using fuzzy logic. Different from previous classifications, our new method categorizes the individual in a continuous space and represents one's personality in a more fine-grained level. We have designed comparative psychological tests for 77 people. The validation experiments on such tests indicate that the fuzzy-logic-based method is not only consistent with the classic MBTI tests (in the sense of defuzzification) but also provides the uncertainty for each personality type. Therefore, it can be viewed as a generalization of the classic MBTI tests and promotes the representation of individual's heterogeneity for fine-grained analytics of digital human.

关键词： Psychology Fuzzy logic Automation Technological innovation Uncertainty Soft sensors Social networking (online) Digital human digital personality fuzzy logic reasoning fuzzy personality recognition Myers-Briggs type indicator (MBTI)

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