Invited Speakers

Prof. Yangmin Xie
Shanghai University, China

Biography：Yangmin Xie received the B.S. and M.S. degrees in mechanical engineering from the School of Mechanical Engineering & Automation, Beihang University, Beijing, China, in 2005 and 2008 respectively. She received the Ph.D. degree in mechanical engineering from the University of Illinois at Urbana-Champaign, Urbana, IL, USA, in 2013. She was a Postdoctoral Associate with the University of Illinois at Urbana-Champaign. She is currently a Full Professor with the School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China. She is also the Deputy Director of the Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, and the Deputy Director of the Shanghai Robotics Institute. Her research interests include intelligent sensing and control of mobile robots, environmental reconstruction, and autonavigation of unmanned systems. Dr. Xie was elected in the Youth Talent Promotion Project by the China Association for Science and Technology, and was the recipient of the Rising Star Program by the Shanghai Science and Technology Committee.

Speech Title: 3D Navigation via Multi-Sensor Fusion: Adaptive Framework and Full-View Perception

Abstract: The development of embodied intelligence highlights mobility and manipulation as its two core dimensions, with navigation serving as the foundation of mobility intelligence. Traditional 2D indoor navigation systems—limited in spatial modeling capability and environmental adaptability—are no longer sufficient for robots operating in complex, high-dynamic 3D environments. To address this challenge, we developed a multi-sensor fusion–based 3D navigation framework that integrates heterogeneous perception sources through a hot-swappable architecture and adaptive fusion strategy.
The proposed system tackles three major bottlenecks in current navigation technologies: poor environmental adaptability, weak terrain compliance, and limited field of view. We introduce a unified framework combining multi-layer topological path planning, terrain-adaptive control, and spherical-pixel–based panoramic perception to achieve distortion-free, full-angle sensing and robust navigation performance across unstructured terrains.
Experimental results in diverse logistics and autonomous mobile platforms demonstrate significant improvements in dynamic scene adaptation and path efficiency. The framework effectively reduces hardware coupling, and enables seamless among heterogeneous platforms. It contributes to establishing a generalized 3D navigation paradigm capable of supporting next-generation intelligent robotics and embodied AI applications.

Prof. Yu Zhao
Northwestern Polytechnical University, China

Biography：Prof. Yu Zhao is a tenured professor and doctoral supervisor at Northwestern Polytechnical University, recognized as a National Outstanding Youth Fund recipient and a high-level talent of Shaanxi Province. Prof. Zhao has long been committed to research in cluster intelligence collaborative decision-making, as well as planning and control of multi-agent systems, achieving remarkable academic accomplishments. He has published over 100 academic papers, with 18 featured in top-tier control journals IEEE Transactions on Automatic Control and Automatic (including 8 full papers), a testament to the depth and significance of his work. He has led more than 10 key scientific research projects and earned numerous prestigious honors, such as the Second Prize of National Defense Science and Technology Progress, the First Prize of Shaanxi Provincial Graduate Education Achievement Award, the National Best Paper Award on Complex Networks, and twice the Shaanxi Provincial Excellent Academic Paper Award in Natural Sciences. Additionally, he has been consistently ranked among the world’s top scientists, underscoring his prominent standing in the global academic community.

Speech Title: Dynamic Average Consensus over Asymmetric Networks: A New Integral Surplus-Based Approach

Abstract: Motivated by the engineering task of cooperative encirclement for dynamic non-cooperative targets, this study identifies the key scientific problem of dynamic average consensus over asymmetric networks. To this end, a new integral surplus-based framework is established. By analyzing the eigenvalues and eigenvectors of the networked matrix, this framework resolves the challenge of center estimation in multi-target cooperative encirclement over directed networks. As a further extension, an “integral surplus + state decomposition” privacy-preserving communication mechanism is designed to tackle communication privacy and security concerns. Subsequently, considering spatiotemporal coordination accuracy, an “integral surplus + prescribed-time” cooperative planning and formation-based tracking control scheme is developed. Finally, the proposed approach is applied to the high-precision, timed and positioned space flying net cooperative encirclement mission for dynamic non-cooperative targets.

Prof. Ze Tang
Jiangnan University, China

Biography：Ze Tang received the Ph.D. degree in Electrical Engineering from Yeungnam University, Republic of Korea, in 2017. He joined Jiangnan University, China in 2017 as an Associate Professor, where he is currently a full Professor and Doctoral Supervisor. In 2016, he was a Visiting Fellow with the School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia. His current research interests include control of complex dynamical systems and control and applications of Internet of things. He was selected as an outstanding young backbone teacher under the Blue and Green Project of Jiangsu Province's universities. He is currently the Deputy Director of Jiangsu Province Engineering Research Center of Industrial Internet Intelligent Optimization Manufacturing. He serves as an Associate Editor for International Journal of Control, Automation and Systems, the Youth Editor of the Journal of System Simulation, Unmanned Autonomous System, Journal of Nanjing University of Science and Technology, and the Young Consulting Expert for the journal Information and Control.

Speech Title: Recent Study on Impulse Control and Synchronization of Coupled Neural Networks

Abstract: Coupled neural networks are a special type of complex networks, and their synchronization control has important applications in fields such as aircraft detection, image processing, and natural language processing. This presentation mainly focuses on the impulsive synchronization and control of several types of coupled neural networks. Firstly, considering parameter mismatches and the mutual influence among different clusters, the problem of quasi-synchronization for coupled neural networks with mixed time-varying delays is investigated, where a pinning impulsive controller is designed. Sufficient conditions for quasi-synchronization are provided. Secondly, taking into account the random uncertainties in communication channels, the quasi-synchronization problem of a class of coupled neural networks with time-varying delays is studied, where a novel distributed event-triggered impulsive control method is proposed. Thirdly, an impulsive time-window scheme is introduced to study the saturated distributed impulsive synchronization problem of a class of directed coupled neural networks with unbounded proportional delays and distributed delays. By utilizing the improved compact convex-hull representation principle, sufficient conditions for local exponential synchronization within the region of attraction are given. Finally, considering the influence of network attacks and the saturation constraints of impulsive control signals, the mean-square synchronization problem of a class of coupled neural networks under deception attacks is investigated by proposing a hybrid event-triggered controller, which effectively solves the actuator saturation problem in discrete-time control signals.

Assoc. Prof. Laihao Yang
Xi'an Jiaotong University, China

Biography：Dr. Laihao Yang received his Ph.D. Degree in the School of Mechanical Engineering from Xi’an Jiaotong University. He was a research fellow of the Structural Dynamics & Acoustic Systems Laboratory (SDASL). He is now in the faculty of the School of Mechanical Engineering at Xi’an Jiaotong University. His research interests include nonlinear vibration modeling and analysis, data-driven structural dynamics, compressive sensing, interpretable AI, and soft robotics. He is the recipient of the first Prize of Science and Technology Award of Shaanxi Higher Education Institutions and the best paper award of CMMNO 2024. He is currently serving on the Junior Editorial Board Member of Soft Science and Robot Learning, the council member of the Professional Committee for Dynamic Testing in the Chinese Society of Vibration Engineering.

Speech Title: Minimally Invasive Robotic Systems for In-situ Maintenance of Aero-Engines

Abstract: In-situ mini-invasive maintenance (inspection and repair) is crucial for the safety insurance and economic purposes of aero-engines. However, the conventional borescope inspection and bore-blending tool both suffer from low slenderness and flexibility, thus impeding the achievement of the goal of highly efficient and intelligent in-situ maintenance for aero-engines. The demand for cross-scale manipulation and multiple functionalities of the in-situ maintenance mission poses new challenges on the way to this goal. This report addresses these issues by proposing a novel in-situ minimally invasive marsupial robotic system for aero-engine maintenance, which contains continuum robots with large length-to-diameter ratios and crawling robots with the capacity for crawling on complex surfaces. Their cooperation enables cross-scale and multifunctional manipulation capacity under unstructured and confined spaces. Specifically, the development of the phylogenetically designed continuum robotic system with high torsion resistance, high load capacity, and excellent compliance will be comprehensively presented from the design principle to motion control. Subsequently, the crawling robot inspired by multistable mechanisms such as origami will showcase how to perform inspection and maintenance tasks in unstructured environments. At last, a brief conclusion and outlook of this report will be given.

Assoc. Prof. Jiusi Zhang

University of Electronic Science and Technology of China, China

Biography：Jiusi Zhang received the B.E. degree in automation from Harbin Engineering University, Harbin, China, in 2019, and the M.Sc. and Ph.D. degrees in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2021 and 2024, respectively. He is currently an Associate Professor with the School of Automation Engineering, University of Electronic Science and Technology of China. His research interests include fault diagnosis and prognosis, predictive maintenance, reliability and safety, data-driven monitoring and optimization, prognostics and health management, and their intelligent applications in industrial systems.

Speech Title: Recent Study on Preventive Maintenance for Complex Industrial Systems

Abstract: This report examines active safety for complex industrial systems, with remaining useful life (RUL) prediction as the foundation for preventive maintenance and risk-aware decision making. The discussion is organized around some persistent challenges. First, information insufficiency: limited labels, domain shifts, and unknown operating conditions require strategies that make effective use of small, heterogeneous datasets while respecting data sovereignty and facilitating privacy-preserving collaboration. Second, focusing on model robustness and sensitivity, performance is improved and computational costs are reduced by optimizing data input and model configuration. Third, information integration: multi-source fusion and spatiotemporal coupling are leveraged to capture early-warning cues and extend prediction horizons. Finally, pathways are articulated for translating RUL estimates into maintenance scheduling, and life-extension policies operational management.

Assoc. Prof. Xu Liang
Beijing Jiaotong University, China

Biography：Xu Liang received the B.E. degree in automation from Central South University, Changsha, China, in 2013, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2020. He is currently an Associate Professor with the School of Automation and Intelligence, Beijing Jiaotong University. His research interests include rehabilitation robot, orthopedic robot, human–machine interaction, and intelligent systems. Dr. Liang was elected in the Beijing Nova Program, and was the recipient of the first prize of technical invention of the Chinese Association of Automation, the Grand Prize of the National Robot Patent Innovation and Entrepreneurship Competition, the Gold medal of the Geneva International Invention Exhibition, and the outstanding paper award of the "Robot" journal. He serves as an excellent early career editorial board member of the “Biomimetic Intelligence and Robotics” journal, and the deputy secretary general of intelligent wearable technology professional committee of Chinese Institute of Command and Control.