Bio: Vice Dean of the School of Automation at Nanjing University of Science and Technology, PhD Advisor, Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He earned his Ph.D. from the University of Lille in France. His current research focuses on hybrid systems theory and applications, data-driven model-free control, state observer design, networked visual servo control, optimal control of renewable energy systems, as well as the design and intelligent control of rehabilitation and power-assisted mechanical exoskeleton robots. He has led over 30 national and provincial-level projects, including key international scientific and technological innovation collaboration projects under the National Key R&D Program, National Natural Science Foundation of China projects, and the Sino-French Government Cai Yuanpei Program. To date, he has published 277 papers in various international journals and conferences, including 147 high-quality SCI-indexed papers as first or corresponding author, authored two monographs, and filed 15 invention patents.

Abstract: With the advancement of new technology and industrial transformation, complex mechatronic systems (CMS) which integrate advanced sensors, control algorithms, artificial intelligence, and other technologies are being widely applied in industrial production, smart manufacturing, power-assisted systems, and surgical medicine robots. Compared to traditional mechatronic systems, CMS possess intelligent sensing, execution, and control capabilities, enabling them to better adapt to complex and dynamic working environments and task requirements. This significantly enhances industrial mass production efficiency and standards, driving the high-quality development of the real-world intelligent manufacturing industry. However, due to their complex structure and multiple degrees of freedom, CMS are susceptible to input nonlinearities such as backlash, input saturation, dead zones, actuator failures, complex friction, and quantization, making it highly challenging to achieve high-precision trajectory tracking control. Therefore considering the referred CMS affected by different input nonlinearities and frictions, under an ultra local model based data-driven model-free control framework, this lecture focuses on the introduction and development of a new data and intelligent technique driven model-free adaptive trajectory tracking prescribed performance control.

Bio: Mingbo Niu, Professor and PhD advisor, is the Director of the Shaanxi International Innovation Research Centre for Transportation-Energy-Information Fusion and Sustainability, and Heading the IVR Low Carbon Research Institute, Chang'an University, Shaanxi, China. He is a Licensed Professional Engineer in British Columbia. He has coauthored more than 100 Institute of Electrical and Electronics Engineers (IEEE), Elsevier, and Optical Society of America (OSA) papers and supervised numerous projects. His research interests include the internet of vehicles (IoV), vehicle-to-road (V2R) infrastructure, cooperative microgrids, massive multiple input multiple outputs (M-MIMO), image signal processing, low-carbon smart cities, transportation-energy fusion, and renewable power theory. Dr. Niu is a specialized committee member of the Internet of Things (IoT) Committee at the China Institute of Communications (CIC). He contributes to the National Key R&D Projects on Renewable HWY Transportation Energy Systems. He served as the General Chair of the 3rd and 4th International Conference on Intelligent Traffic Systems and Smart City, and an Academic Editor for InTech Publishing, EU. He received numerous awards, including a Chinese Government Award (CGA), two University of British Columbia University Graduate Fellowships (UGFs), and a Huawei Tech Ltd., Special Fellowship. He has ever worked with the State Key Laboratory on Underwater Information and Signal Processing, China.

Abstract: In order to address the challenges incurred from the decrease in operational efficiency of intelligent transportation systems (ITS) and distribution networks (DN), with the worldwide spread adoption of electric vehicles, it has become particularly urgent to explore collaborative optimization strategies for electric energy systems within a new transportation energy utilization framework. How can we achieve intelligent collaboration among electric vehicles, smart transportation systems, and distribution networks at all levels, thereby enhancing the overall efficiency of the electric transportation system and reducing operational costs? This remains a key issue that needs to be addressed. Through an analysis of the key coupling elements between transportation networks and power networks, we propose a transformation of the role of electric vehicles in promoting the development of smart cities and the construction of sustainable energy systems. This will further enhance the peak-valley resilience and traffic efficiency of smart transportation systems, facilitating the important industry transformation process of the transportation sector from high carbon emission to low carbon emission, and ultimately to a zero carbon new system.

Bio: Prof. Yang Sun, born in August 1979, doctor, professor, doctoral supervisor, vice president of the Science and Technology Research Institute of Hebei University of Engineering, director of the Key Laboratory of Intelligent Vehicles in Handan City, member of CAA Parallel Intelligence Special Committee of the Chinese Society of Automation, member of the National Teaching Guidance Committee for Automotive Service Engineering, member of the Engineering drawing Society of Hebei Province, and member of the Artificial Intelligence and Big data Professional Teaching Committee of the China Machinery Industry Education Association, Member of the Mechanical Engineering Expert Committee of the VE, member of the Chinese Society of Automation, member of the Chinese Society of Automotive Engineering, young academic backbone of Hebei University of Engineering, member of the Academic Committee of the College of Mechanical and Equipment Engineering, Expert reviewers for journals such as IEEE Transactions on Industrial Informatics, World Journal of Engineering, International Journal of Mechanical Engineering and Applications, Advances in Mechanical Engineering, Journal of Zhejiang University, the Journal of Weapon Equipment Engineering. In June 2014, he received a doctor's degree in mechanical engineering from Beijing Institute of Technology. In recent years, have led or participated in 4 national level scientific research projects and 12 provincial and ministerial level scientific research projects; Obtained 5 national patents; Published over 50 papers, including over 30 included in SCI and EI; Received 1 Provincial Science and Technology Progress Award, 2 Provincial Teaching Achievement Awards, and 2 Municipal Science and Technology Progress Awards.

Abstract: Recent research efforts were presented in the following areas: vision-based object detection; multi-sensor fusion for object detection; SLAM mapping and localization; and stability control for line-guided chassis.

Bio: Professor, Ph.D., Doctoral/Master's Supervisor. Member of the Vehicle Dynamics Branch and Suspension Branch of the China Society of Automotive Engineers. Selected for Jiangsu Provincial Talent Program. Review Expert for the National Natural Science Foundation of China. Postdoctoral Fellow at Jilin University School of Automotive Engineering. Visiting Scholar at Virginia Tech and Clemson University. He has led one National Natural Science Foundation of China (NSFC) General Program project and one Jiangsu Provincial Natural Science Foundation General Program project. He has led or participated in over 30 vertical and horizontal research projects and more than 10 educational reform projects. He received one Third Prize in Military Scientific and Technological Progress Award, holds over 20 authorized invention patents, and authored over 50 academic papers as first author, with more than 20 papers indexed by SCI and EI. He was invited by CRC Press (Taylor & Francis Group) to write a monograph and has edited one undergraduate textbook. Primary research areas: Vehicle system dynamics and control; Intelligent unmanned vehicles; Motor-based active suspension technology; Vehicle and component design and optimization.

Abstract:This report proposes a new method for controlling vehicle body height and posture adjustment utilizing semi-active actuators (specifically through asymmetric damping control). The core principle involves capturing vibration energy generated during vehicle operation by independently regulating the damping coefficients of the extension and compression strokes, thereby shifting the equilibrium position of the vibration isolator. To address the strong nonlinearities in the system, the research team employed the Homotopy Analysis Method (HAM) to obtain precise analytical solutions. The feasibility of this approach was verified through simulation and an experimental system using magneto-rheological dampers, which confirmed that the lifting height is determined by road excitation amplitude, frequency, and the asymmetric damping ratio. Characterized by low energy consumption, low cost, and a simple structure, this method offers an efficient alternative for height and posture control in specific scenarios such as short-term obstacle crossing, high-speed turning, emergency avoidance, and rollover prevention.