Chair: Mei-Yung Chen, National Taiwan Normal University, Taiwan, ROC

Vice Chair: Chia-Wen Chang, National Ilan University, Taiwan, ROC

This session focuses on the theoretical and practical applications of intelligent control in modern technology, combining artificial intelligence with control theory to explore how to improve the performance and flexibility of control systems in complex and uncertain environments. Intelligent control encompasses a variety of AI methods, including neural networks, fuzzy logic, reinforcement learning, and evolutionary algorithms, and can address the challenges of traditional control strategies in nonlinear and highly dynamic environments.

1. Autonomous System Control: Intelligent strategies for real-time decision-making platforms such as drones and self-driving cars;
2. Human-Robot Collaborative Control: Research on how AI can improve the interaction between operators and systems;
3. Cross-disciplinary Case Studies: Practical AI control in energy management, medical systems, and manufacturing;
4.Future Trends and Challenges: Exploring the development directions and challenges of AI control in security, explainability, and resource-constrained environments (such as low-power devices).

Chair: Huanghe Zhang, Associate Professor, Shandong University, China

Vice Chair1: Fukai Zhang, Associate Professor, Shandong University, China

Vice Chair2: Weiming Wu, Associate Professor, Shandong University, China

This special session advances trustworthy, data-driven methods that close the sensing–learning–control loop across healthcare, robotics, manufacturing, and other industrial systems. We welcome contributions on multimodal perception, system identification, robust/safe control (including verification and certification), and real-time deployment—especially works demonstrating cross-domain transfer, reliability, and measurable safety in the loop.

1. Multimodal sensing & representation learning (self-/semi-supervised, foundation models);
2. System identification & dynamics learning (physics-informed ML, Koopman, neuro-symbolic);
3. Learning-based control (MPC, robust/adaptive, RL/IL) and runtime assurance/verification;
4. Uncertainty quantification, calibration, and certified safety for cyber-physical systems
5. Human-in-the-loop control, intent inference, shared autonomy, ergonomics/biomechanics;
6. Edge/real-time deployment, hardware-aware AI, reliability–latency tradeoffs;
7. Applications in medical imaging & biosignals (EMG/EEG), rehabilitation/exoskeletons, process & energy, robotics & manufacturing, and aerospace/mobility.