Admittance-Based Adaptive Predefined-Time Fault-Tolerant Control for Physical Human–Robot Interaction With Prescribed Tracking

Compliant tracking control in physical human–robot interaction (pHRI) systems is becoming increasingly important. However, without force sensors, the overlap of multiple uncertainties and contact forces poses significant challenges to compliant tracking within admittance-based frameworks. Meanwhile, actuator faults in robots can degrade tracking performance, leading to pHRI system instability and safety hazards. To this end, the compliance control issue for pHRI systems with prescribed tracking under actuator faults is investigated. First, we treat multiple uncertainties and contact forces as an entirety and design an adaptive online updating law to reconstruct operator’s behavior. Second, a novel prescribed performance function (PPF) is designed and integrated into the nonsingular virtual control term. The main advantage of the developed PPF lies in its ability to overcome the feasibility limitations of other PPFs, extending its applicability to arbitrary initial values. Following this, the adaptive technique is employed to solve the unknown control gain issue induced by actuator faults. Compared with finite-/fixed-time controllers, the developed approach can guarantee the transient and steady-state performance of the pHRI system under actuator faults, with convergence time being independent of initial values. Finally, numerous simulations and experiments are conducted to demonstrate the viability and feasibility of the developed approach.