Event-triggered adaptive control for upper-extremity therapeutic robot in active-assist mode: A simulation study

Abstract

Upper extremity rehabilitation exercises are essential for individuals recovering from injuries or conditions that affect their arm and hand functionality. Robot-assisted therapy has gained popularity as it offers precise control, objective assessment, and customizable treatment programs. However, several challenges persist, including uncertainties in the patient’s and robot dynamics, limited communication, and the need to maintain a compliant patient-robot interaction. Therefore, an event-triggered adaptive backstepping (ETAB) admittance control strategy is proposed in this work to address these challenges. Initially, the framework of the robot-assisted therapeutic process is briefly explained. The architecture of the proposed control strategy is formulated with two control modules. Thereafter, the adaptive backstepping technique is employed with an online adaptation law to deal with dynamic uncertainties. Moreover, the problem of limited communication is handled using the proposed design of a Lyapunov-based event-triggered mechanism. The admittance controller is integrated to maintain a compliant patient-robot interaction and consider the participation of the patient in the therapeutic sessions. The effectiveness of the proposed control strategy is verified using an end-effector type rehabilitation robot performing two different rehabilitation exercises. Furthermore, a comparative performance analysis is carried out with the proportional-integral-derivative controller (PID) and the adaptive sliding mode controller (ASMC). Based on the simulation runs, the proposed controller has shown promising tracking behavior, appropriate compliant interaction, and considerable reduction in the transmitted signals during the passive and active-assist training exercises.