Modeling, Control and Analysis of Mixed Traffic Flow with Autonomous Vehicles
Session Chair: Wenxing Zhu, Shandong University, China
Guanghan Peng, Guangxi Normal University, China
Key Words: Mixed Traffic Flow, Autonomous Vehicles, Stability Analysis, Controller Design, Energy Consumption Analysis
Information: With the rapid development of the social economy and science technology, autonomous vehicles have become a research hotspot in the field of modern transportation, and the mixed operation of manual driving and autonomous vehicles will become an inevitable trend in the future. There are a lot of physicists and scholars interested in this field for many years. In addition, under the dual carbon goal in our country, the energy consumption of mixed traffic flow with vehicles driven by multiple energy sources is also a hot issue having attracted the attention of lots of experts and scholars. Through the organization of this session, we hope to attract experts in this field to share the latest research results and feasible solutions, and promote the solution of various complex problems in the field of mixed traffic flow.
Topics of interest include but are not limited to:
- Modeling on Mixed Traffic Flow
- Energy Consumption Analysis on Various Vehicles
- Controller Design Method on Mixed Traffic Flow
Submission Deadline: April 28, 2026 (the first round)
Advances in Intelligent Perception, Decision-making and Control for Unmanned Systems
Session Chair: Panlong Wu, Nanjing University of Science and Technology, China
Baichun Gong, Nanjing University of Aeronautics and Astronautics, China
Information: The session will focus on the core aspects of unmanned systems, including target detection and recognition, situational awareness, autonomous decision-making, and collaborative control, with an emphasis on addressing critical challenges such as low efficiency in multimodal data fusion, delays in behavioral cognition and intent inference, poor interpretability of strategies, and insufficient stability in group consensus control. Furthermore, it will explore practical applications of machine learning algorithms—including deep learning, reinforcement learning, and large-scale models—in prediction, decision-making, and intelligent interaction. This session aims to bring together researchers from diverse disciplinary backgrounds to advance the theoretical development and practical implementation of unmanned system control.
Topics of interest include but are not limited to:
- Integration theory of intelligent perception and autonomous decision-making
- Autonomous target detection and recognition in open environments
- Task allocation and path planning
- Autonomous control of unmanned systems
- Machine learning algorithms and their trustworthy applications
Submission Deadline: April 28, 2026 (the first round)
Progress and Innovations in Multi-Source Information Fusion, Navigation, and Guidance Technologies
Session Chair: Shuai Chen, Nanjing University of Science and Technology, China
Key Words: Multi-source information fusion; filtering algorithms; cooperative navigation and guidance; intelligent navigation; positioning, navigation, and timing (PNT)
Information:
Advances in the information era have imposed higher performance requirements on navigation systems, while also introducing new methods and challenges. This conference aims to discuss innovations and engineering practices in multi-source information fusion algorithms, data-driven intelligent navigation algorithms, and image-recognition- and matching-based localization methods, in order to improve the accuracy and robustness of position, attitude, and timing information. In addition, the conference will further explore integrated implementations of cooperative navigation, guidance, and control. We hope to jointly examine the latest research achievements and future development trends in navigation–control–guidance integration under complex conditions such as high dynamics, severe electromagnetic interference, and weak textures, thereby promoting solutions to high-precision position, attitude, and timing acquisition as well as navigation and guidance problems for aircraft, spacecraft, automobiles, robots, ships, underwater vehicles, and other platforms operating in diverse environments, including low-altitude, high-altitude, deep-space, urban canyons, indoor and underground spaces, sea surface, and underwater settings.
Topics of interest include but are not limited to:
- Innovations and applications in cooperative navigation and guidance
- Multi-source information fusion algorithms and engineering applications
- Image recognition and matching algorithms in complex environments
- Data-driven intelligent navigation methods and innovations
- Integrated PNT and micro-PNT technologies
Submission Deadline: April 28, 2026 (the first round)
Design, Control and Optimization of Green Hydrogen Integrated Energy Network System
Session Chair: Yunfeng Peng, Shanghai Jiao Tong University, China
Longze Wang, North China Electric Power University, China
Key Words: Green Hydrogen System, Industrial Control Network, Power Control, Energy Management, System Modeling
Information: Under the background of global decarbonization, as the main energy carrier, comprehensive energy system of green hydrogen has been paid widespread attention. Due to random change of weather, the output of photovoltaic and wind power system show significant randomness. Grid regulation, hydrogen energy vehicle scheduling and changing hydrogen supply demand of chemical plants make the problem of untimely and unreasonable hydrogen absorption increasingly prominent. Therefore, the imbalance on the supply and demand side leads to the low energy efficiency of the system. Digital modeling technology is considered to be an effective tool to guide system management. The construction of information perception, transmission and decision-making network of green hydrogen integrated energy network system based on digital model is expected to improve energy efficiency. Through the power control of hydrogen-to-electricity conversion, the management of multi-energy flow in the park and optimal scheduling of hydrogen industry chain, the maximum consumption of power generation can be realized, as well as reliable production, manufacturing, operation and maintenance. This topic will focus on the application of digital modeling and intelligent control methods to improve the efficiency of green hydrogen integrated energy network system.
Topics of interest include but are not limited to:
- Digital modeling of green hydrogen integrated energy network system
- Information perception, communication, control and calculation of green hydrogen integrated energy network system
- Power control of hydrogen-electric integration
- Management strategy of hydrogen, electricity and thermal energy in zero-carbon park
- Optimal scheduling of hydrogen production, storage, transport and hydrogen injection
Submission Deadline: April 28, 2026 (the first round)
Advanced Control Theory and Applications of Intelligent Systems
Session Chair: Jianqi Chen, Nanjing University, China
Di Zhao, Nanjing University, China
Key Words: Intelligent System, Adaptive Control, Multi-Agent Systems, Robust Control, Safety-Critical Control
Information: This session focuses on advanced control theory for intelligent systems and its cutting-edge applications. With the rapid development of artificial intelligence, machine learning, and big data technologies, the application of intelligent systems has expanded across various fields, including autonomous driving, smart manufacturing, robotics, and the Internet of Things. The session will concentrate on advanced control methods in intelligent systems, such as adaptive control, robust control, and coordination and optimization in multi-agent systems, while also addressing the challenges and real-world applications of these theories. We invite scholars and researchers worldwide to share their latest results on innovations in control theory for intelligent systems, application cases, and technical implementations, contributing to the theoretical development and widespread application of intelligent systems.
Topics of interest include but are not limited to:
- Adaptive Control and Collaborative Optimization for Intelligent Systems
- Robust Control in Multi-Agent Systems
- Control Theory and Practice in Smart Manufacturing
- Challenges and Solutions in Delay Systems and Networked Control
- Safety Verification and Control for Interconnected Systems
Submission Deadline: April 28, 2026 (the first round)
Learning Control and Games of Networked Systems
Session Chair: Bingchang Wang, Shandong University, China
Key Words: Stochastic games, Optimal control, Multiagent systems, Reinforcement learning, Stackelberg games, Mean field games
Information: This invited session focuses on learning control and games of networked systems, and includes topics of stochastic games, optimal control, multiagent systems, reinforcement learning, Stackelberg games and mean field games.
Topics of interest include but are not limited to:
- Stochastic Optimal Control
- Data-Driven Stochastic Differential Games
- Mean Field Stackelberg Games
- Optimal Control of Networked Dynamic Systems
- Stochastic Optimal Control of Networked Systems
- Mean Field Policy Iteration Algorithms
- Inverse Reinforcement Learning for Games
- Adaptive Distributed Learning Control
Submission Deadline: April 28, 2026 (the first round)
Secure State Estimation and Control for Networked Systems
Session Chair: Yong Xu, Guangdong University of Technology, China
Xiaoqiang Ren, Shanghai University, China
Key Words: Networked control systems, Secure state estimation, Resilient control, Adversarial attacks, Defensive transmission scheduling
Information: Networked systems such as cyber-physical systems, industrial control systems, intelligent transportation systems, and multi-agent systems are increasingly exposed to cyber threats due to the wireless communication networks. Malicious attacks, including false data injection, denial-of-service, eavesdropping and deception attacks, can severely degrade state estimation accuracy and compromise control performance. This session focuses on recent advances in secure and resilient state estimation and control for networked systems under adversarial and uncertain environments. The session aims to bring together researchers working on control theory, wireless communication, algorithm design, and practical implementations to enhance the robustness, security, and trustworthiness of networked control systems.
Topics of interest include but are not limited to:
- Secure and resilient state estimation under cyber attacks
- Attack detection, isolation, and identification in networked systems
- False data injection, denial-of-service, eavesdropping and deception attacks
- Secure control and stabilization under adversarial environments
- Defensive transmission scheduling against cyber attacks
- Distributed and cooperative secure estimation in multi-agent systems
- Game-theoretic security analysis
- Privacy-preserving estimation and control
- Applications to smart grids, autonomous vehicles, robotics, and industrial CPS
Submission Deadline: April 28, 2026 (the first round)
Control, Learning, and Games of Complex Systems: Theory and Methods
Session Chair: Yuanhua Ni, Nankai University, China
Key Words: Optimal Control, Learning, Game Theory, Dynamical Systems
Information: This special session focuses on control, learning, and game-theoretic frameworks for complex dynamical systems, addressing fundamental problems in modeling, analysis, and methodology under structural complexity, information constraints, and interacting decision-makers. As modern systems grow in scale and coupling, control theory is increasingly integrated with learning methods and game theory, leading to new analytical paradigms and solution approaches.
The session welcomes theoretical contributions that explore unified perspectives of control, learning, and games, including stability and performance analysis, optimal and robust control, learning-based and adaptive decision-making, and coordination and competition in multi-agent systems. The goal is to advance the theoretical foundations of control and decision-making in complex systems.
Topics of interest include but are not limited to:
- Optimal control theory and methods for dynamical systems
- Learning methods in control systems
- Game theory and decision-making in dynamical systems
- Control and decision-making in multi-agent systems
- Distributed and decentralized optimal control
- Intersections of control, learning, and games
- Interaction and decision-making in networked systems
- Optimization and decision-making in control systems
Submission Deadline: April 28, 2026 (the first round)
Advances in Stability Analysis and Intelligent Control for Stochastic Systems
Session Chair: Weihai Zhang, Shandong University of Science and Technology, China
Tianliang Zhang, Qingdao University of Technology, China
Key Words: Stochastic Systems, Intelligent Control, Stability Analysis, Machine Learning in Control
Information: The stability analysis of stochastic systems and intelligent control is a frontier field in contemporary control theory, especially with the high demands for system stability in complex and uncertain environments. With the rapid development of intelligent control technologies, ensuring the robustness of systems under stochastic disturbances has become an important challenge in both theory and practice. This special session is aimed at researchers, engineers, and students in the fields of control theory and intelligent control technologies, particularly those who focus on the stability analysis of stochastic systems and the application of intelligent control. The research objective of this session is to explore how advanced stability analysis and intelligent control techniques can address uncertainties and disturbances in stochastic systems. Through this research, the aim is to explore effective methods to improve system stability and optimize control strategies in practical applications, thereby advancing the theory and technology in this field.
Topics of interest include but are not limited to:
- Development of adaptive learning control methods for stochastic systems
- Robustness analysis and performance guarantees in the presence of random disturbances
- Stochastic optimal control and adaptive learning control
- Lyapunov-based approaches for stochastic adaptive control
- Convergence and stability analysis in stochastic environments
- Exploration of deep learning and reinforcement learning for stochastic systems
Submission Deadline: April 28, 2026 (the first round)
Modeling, Control, and Optimization for Complex Systems
Session Chair: Baolin Zhang, Qingdao University of Science and Technology, China
Shiyuan Han, Shandong Women's University, China
Guangchen Zhang, North Minzu University, China
Key Words: Complex Systems, Hybrid and Data-driven Modeling, Intelligent and Learning-based Control, Real-time & Robust Optimization, Cyber-physical Systems
Information: Complex systems spanning smart manufacturing, intelligent energy networks, autonomous transportation, advanced robotics, and large-scale cyber-physical infrastructures present significant challenges in design, analysis, and operation. These systems are characterized by high dimensionality, strong nonlinearity, multi-scale dynamics, interconnected subsystems, and inherent uncertainties. Traditional methods often fall short in capturing their intricate behaviors and ensuring efficient, reliable, and adaptive performance. This special issue focuses on advanced methodologies and applications for modeling, control, and optimization of complex systems, and addresses theoretical foundations, algorithmic innovations, and practical implementations aimed at enhancing the understanding, autonomy, and performance of such systems in real-world settings.
Topics of interest include but are not limited to:
- Data-driven, and hybrid modeling approaches of complex systems
- Multi-scale and network-based modeling of control systems
- Learning-based control and AI-driven autonomy
- Event-triggered control strategies
- Multi-objective and dynamic optimization
- Optimization under uncertainty and real-time decision-making
- Co-design of modeling, control, and optimization
Submission Deadline: April 28, 2026 (the first round)
Autonomous Multi-Agent Systems in Robotics: Perception, Decision-making, Control, Learning, and Optimization Methods
Session Chair: Xin Tong, Nanjing University, China
Jie Chen, University of Science and Technology of China, China
Key Words: Multi-Agent Systems, Autonomous Robots, Perception, Decision-Making, Control, Learning and Optimization
Information: This special session focuses on recent advances, challenges, and applications of autonomous multi-agent systems in robotics. It aims to bring together researchers and practitioners to discuss state-of-the-art theories, algorithms, and implementations related to multi-agent perception, cooperative decision-making, motion control, learning-based strategies, and optimization methods. It welcomes contributions covering distributed perception, collaborative planning, robust and safe control, multi-agent reinforcement learning, model predictive control, distributed optimization, task allocation, and real-world deployment in robotic systems. The session seeks to promote cross-disciplinary ideas and foster innovations for building reliable, scalable, and intelligent autonomous multi-agent systems.
Topics of interest include but are not limited to:
- Distributed perception and environment cognition for autonomous multi-agent robotic systems
- Collaborative decision-making and task allocation in multi-robot systems
- Safe, robust and adaptive control of networked multi-agent robotic systems
- Learning-based strategies for multi-agent coordination and motion planning
- Distributed optimization and optimal control in multi-robot systems
- Integration of perception, decision-making, control and learning for autonomous multi-agent systems
Submission Deadline: April 28, 2026 (the first round)
Embodied Intelligent Robotic Joint Technologies: Innovative Design and Applications
Session Chair: Ming Lu, Hunan University of Science and Technology, China
Changzhong Pan, Hunan University of Science and Technology, China
Key Words: Embodied Intelligent Robotics, Joint Design and Optimization, Actuation and Control Technology, Sensor Integration and Feedback, Bio-inspired Structures and Materials, Human-Robot Collaboration and Interaction
Information: Embodied intelligent robotic joint technology is the core foundation for achieving dexterous motion, environmental adaptation, and intelligent interaction in robots. Recent breakthroughs in bionics, flexible actuation, high-precision sensing, and intelligent control algorithms have driven joint design toward lightweight, high dynamic response, and multimodal perception. However, challenges remain in energy efficiency optimization, dynamic stability, and human-robot collaboration safety. This session focuses on cutting-edge technologies in embodied robotic joints, including innovative actuation mechanisms (e.g., muscle-inspired actuators, magnetorheological fluid joints), multimodal sensor fusion, material-structure-control co-design, and dynamic modeling with real-time optimization. It aims to promote applications in industrial automation, medical rehabilitation, and service robotics.
Topics of interest include but are not limited to:
- Intelligent Joint Actuation (Bio-inspired Actuators, Magnetorheological Materials, Soft Actuation)
- High-precision Joint Sensing and Multimodal Data Fusion
- Bio-inspired Joint Structures and Lightweight Material Design
- Dynamic Control Algorithms and Real-time Optimization for Joints
- Safe Interaction and Haptic Feedback in Human-Robot Collaboration
- Application Validation in Industrial, Medical, and Exoskeleton Scenarios
Submission Deadline: April 28, 2026 (the first round)
More conference sessions to be updated soon.