Machine Learning Based Multiple Impedance Control of a Space Free-Flying Robot

This study investigates the application of Machine Learning (ML) to reduce the inherent computational burden in Multiple Impedance Control (MIC) for robotic systems. Impedance Control (IC) is a powerful method for robots to interact with their environment by simultaneously controlling position and force. However, the increasing complexity of control methods such as MIC requires more and more complex computations, which can lead to problems such as increased response time and reduced efficiency in the control system, making implementation on the controller impractical. To address this challenge, this paper proposes a new ML-based method called ML-MIC. This method aims to reduce the computational complexity associated with MIC using ML techniques, thereby improving the efficiency and speed of robotic control systems. The development of ML-MIC not only leads to improved control system performance but also enables more effective implementation of robotic and mechatronic systems.