Special Session VI

The 10th International Conference on Electrical Engineering, Control and Robotics (EECR 2024)

Special Session VI: Control of Neural Networks-Based Nonlinear Systems


Assoc. Prof. Guanyu Lai, Guangdong University of Technology, China
Assoc. Prof. Xiaohang Su, Guangdong University of Technology, China
Assoc. Prof. Hanzhen Xiao, Guangdong University of Technology, China
Prof. Fang Wang, Shandong University of Science and Technology, China

Submission Guideline:

Please submit your manuscript via EasyChair https://easychair.org/my/conference?conf=eecr2024
please choose Special Session VI: Control of Neural Networks-Based Nonlinear Systems


As a powerful tool for modelling complex uncertain systems, neural networks have been widely used in many key areas, e.g., aerospace, high-speed trains, navigation, numerical control machine, industrial robots, power systems, etc. Unlike traditional canonical-form nonlinear system models, however, the relative degrees of the nonlinear system models constructed by neural networks generally depend on system parameters which may not always be available for measurement in practice. This means that the neural networks-based nonlinear system models can have noncanonical forms, for which the existing Lyapunov-based design and analysis approaches are not applicable any longer, and many new control problems and technical difficulties or challenges need to be investigated and addressed.

This special session aims to bring together researchers, scholars and engineers to discuss and share their latest advancements, findings, and experiences in the field.

The session will include, but is not limited to, the following topics:

  • Neural networks

  • System modelling

  • Intelligent control

  • Model predictive control

  • Event-triggered control

  • Adaptive fault-tolerant control

  • Optimal control

  • Uncertain nonlinear systems

  • Stochastic systems

  • Multi-agent systems

  • Network control systems

  • Robotics

This special session aims to foster collaboration, exchange ideas, and explore potential solutions to the existing challenges in control of neural networks-based nonlinear systems. The session will also serve as a valuable platform for networking and knowledge sharing among participants, leading to potential collaborations and future research directions in this exciting and rapidly evolving field.