Human-Inspired Adaptive Optimal Control Framework for Robot-Environment Interaction

Enabling robots with uncertain dynamics to perform human-like adaptive operations in unknown environments remains a significant challenge in robotics research. Drawing inspiration from the dynamic modification of human arm muscles, we propose an innovative adaptive optimal control framework to address this issue. The framework integrates a variable optimal impedance adaptation (VOIA) method and an adaptive bias broad fuzzy neural network (ABBFNN) controller, facilitating adaptive manipulation behaviors in robot-environment interaction tasks. It can adaptively learn the impedance gain of unknown environment in the presence of uncertain robot dynamic model based on different task properties, simultaneously keeping the tracking error and interaction force optimized and minimized. The ABBFNN controller combines adaptive node increments to approximate uncertain dynamic model and introduces additional global bias and adaptive gain adjustment to improve the approximation accuracy and the rate of convergence significantly. VOIA seamlessly integrates a finely tuned proportional-integral-derivative (PID) variable target stiffness and an impact compensator, ensuring accurate responses to varying environmental conditions and improved disturbance rejection. Moreover, a momentum-based force observer is utilized within the framework for interaction force estimation, eliminating the need for force sensors and simplifying the system. Simulations and experiments validate the effectiveness and practicality of the proposed optimal interaction control framework, demonstrating its potential to propel robots toward interactions with unknown environments.