A Robust Human-Robot Collaborative Control Approach Based on Model Predictive Control

Human skill-based robotic control to perform critical manufacturing operations (e.g., repair and inspection for high-value assets) can reduce scrap rates and increase overall profitability in the industrial community. In this study, a human-robotic collaborative control system is developed for accurate path tracking subject to unknown external disturbances and multiple physical constraints. This is achieved by designing a model predictive control with a sliding-mode disturbance rejection term. To rule out the possibility of the constraints violation caused by external disturbances, tightened constraints are formulated to generate the control input signal. The proposed controller drives the robotic system remotely with enhanced smoothness and real-time human modification on the outputted performance so that the human experience can be fully transferred to robotic systems. The efficacy of the proposed collaborative control system is verified by both Monte-Carlo simulation with 200 cases and experimental results including tungsten inert gas welding based on a universal robot 5e with 6 degree-of-freedom.