CPS: Small: Real-Time Machine Learning-based Control of Human Cyber-Physical Balance Systems

The goal of this project is the advancement of machine learning dynamic models and real-time control systems for human cyber-physical balance systems. Ranging from biped walkers and human bicycle riding to human-controlled helicopters, human cyber-physical balance systems maintain challenging tasks of simultaneously trajectory-tracking and unstable platforms balancing. Although many physical models were developed in past decades, it is still challenging to safely and effectively operate these human-in-the-loop balance machines in highly variable, uncertain environments. This project will develop machine learning-based mathematical models and robust control strategies for human cyber-physical balance systems. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students into research. Human cyber-physical balance systems involve human movements as physical and forceful interactions with unstable, underactuated platforms. It is challenging to capture and control physical human-machine or human-robot interactions in complex, uncertain environments. This project will focus on: (1) development of machine learning-based models and characterization for human cyber-physical balance systems; (2) development of new hardware/software co-design accelerated learning-based real-time control to handle human cyber-physical balance system dynamics in highly variable, uncertain environments; and (3) robotic testbeds development, experimental validation and performance evaluation. The integration of data-driven model and learning-based control strategies, along with the hardware/software co-design enabled real-time implementation, provides new perspectives on performance enhancement of safety-critical or mission-critical cyber-physical systems in dynamic, uncertain environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.