Observer-Based Fuzzy Adaptive Admittance Control for Unknown Environment-Coupled Physical Human–Robot Interaction With Prescribed Tracking

To meet the multi-dimensional modulation requirements of enabling robots to simultaneously achieve compliant adaptation to human time-varying motions and precise force tracking in unknown environments under physical human-robot-environment interaction (pHREI) scenarios, this paper proposed a fuzzy adaptive admittance control (FAAC) strategy that integrates disturbance observation and unified performance guarantees within a force-position dual-loop structure. In the force control outer loop, an extended state observer (ESO) is constructed to compensate for disturbances arising from uncertain human intentions and unstructured environmental geometry, while a PID-based admittance enhancement scheme is introduced to improve dynamic responsiveness. In the position control inner loop, a novel barrier function is embedded into the backstepping framework to systematically regulate the convergence rate, transient overshoot, steady-state accuracy, and global performance of the trajectory tracking error. Moreover, a fuzzy logic system (FLS) is employed to approximate the lumped model uncertainty, which in turn strengthens the robustness of the control algorithm. The asymptotic stability of the closed-loop system is rigorously established via the Lyapunov analysis criterion. Experiments involving trajectory tracking, planar cutting, and curved-surface carving conducted on an actual robotic platform demonstrate that the proposed method not only realizes superior force-position coordinated control, but also accommodates flexibility in physical human-robot interaction (pHRI) and smoothness in physical robot-environment interaction (pREI).