A Bilateral Teleoperation System With Learning-Based Cognitive Guiding Force

This article investigates the problem involving a fuzzy logic system (FLM)-based bilateral teleoperation system with a unified impedance/admittance structure and learning-based assisted cognitive guiding force, enhancing a smooth transition between augmentation and autonomous motion on the master side. The impedance structure regulates the impedance and admittance via the parameters determined by the FLM and, besides the variable impedance during interaction with the environment, the fuzzy
$Q$
-learning algorithm generates an assisted cognitive guiding force to guide the trainee operator in free space for reaching the interaction surface smoothly and steadily. The degeneration of tracking performance in the bilateral teleoperation system caused by time delay is analyzed and reduced via a mixed-type error. Experimental results verify the stability analyses, and under the proposed control scheme, the human–robot–environment interaction is smooth and stable.