Human-Inspired Adaptive Optimal Control Framework for Robot-Environment Interaction
该研究提出一个受人类手臂肌肉动态调节启发的自适应最优控制框架，结合可变最优阻抗适应方法和自适应偏置宽模糊神经网络，使机器人在未知环境中实现类人自适应操作，同时优化跟踪误差和交互力。
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.