In the Intelligent System Design Lab, Department of Robotics and Mechatronics, School of Science and Technology for Future Life, Tokyo Denki University, we are engaged in research aimed at improving well-being. The concept of well-being pursued by our country refers to the state of being “in a good condition” both individually and societally. This notion is not unique to our country but is also applicable internationally. The future societal framework is being evaluated with key terms such as “human-centered,” “sustainable” (i.e., enduring rather than transient), and “flexible adaptability” (i.e., resilience). In our country, issues such as transportation problems due to labor shortages and healthcare challenges in depopulated areas are prevalent. We aim to address these problems through automation driven by robotics and mechatronics technology. A distinctive feature of our approach is not merely the automation of machines but the management of systems that integrate humans, robots, mechatronics, machines, and information via networks. These systems are referred to as Cyber-Physical Systems (CPS), which enable functionalities such as remote healthcare and remote exploration by allowing multiple geographically dispersed individuals to operate multiple machines. However, CPS face challenges such as network capacity limitations, data transmission delays, and data loss, which can reduce the real-time performance of machine operations. Therefore, the development of new control strategies is needed. The key to addressing these challenges in CPS lies in estimation, prediction, and control. Our goal is to enhance the control performance of machines within CPS by proposing optimal control design methods that incorporate learning-based prediction and estimation. We aim to demonstrate the effectiveness of these methods through simulations and experiments, with the intention of contributing to the improvement of well-being.

Research Keywords: Well-Being, Cyber-Physical Systems, Networked Control Systems, Robotics, Mechatronics, Machine Learning, Reinforcement Learning, Estimation Theory, Motion Control, Model Predictive Control, and Gaussian Process.