An Adaptive and Hybrid End-Point/Joint Impedance Controller for Lower Limb Exoskeletons

Abstract

Assist-as-needed (AAN) algorithms for the control of lower extremity rehabilitation robots can promote active participation of patients during training while adapting to their individual performances and impairments. The implementation of such controllers requires the adaptation of a control parameter (often the robot impedance) based on a performance (or error) metric. The choice of how an adaptive impedance controller is formulated implies different challenges and possibilities for controlling the patient's leg movement. In this paper, we analyze the characteristics and limitations of controllers defined in two commonly used formulations: joint and end-point space, exploring especially the implementation of an AAN algorithm. We propose then, as a proof-of-concept, an AAN impedance controller that combines the strengths of working in both spaces: a hybrid joint/end-point impedance controller. This approach gives the possibility to adapt the end-point stiffness in magnitude and direction in order to provide a support that targets the kinematic deviations of the end-point with the appropriate force vector. This controller was implemented on a two-link rehabilitation robot for gait training—the Lokomat®Pro V5 (Hocoma AG, Switzerland) and tested on 5 able-bodied subjects and 1 subject with Spinal Cord Injury. Our experiments show that the hybrid controller is a feasible approach for exoskeleton devices and that it could exploit the benefits of the end-point controller in shaping a desired end-point stiffness and those of the joint controller to promote the correct angular changes in the trajectories of the joints. The adaptation algorithm is able to adapt the end-point stiffness based on the subject's performance in different gait phases, i.e., the robot can render a higher stiffness selectively in the direction and gait phases where the subjects perform with larger kinematic errors. The proposed approach can potentially be generalized to other robotic applications for rehabilitation or assistive purposes.

Effects of selectively assisting impaired subtasks of walking in chronic stroke survivors

“…Some of these current AAN controllers either set a specific assistance level for the whole gait cycle, or they adjust the assistance for each instance of the gait cycle (e.g. each percentage) [ 7 , 9 , 12 ]. Others focus on assisting specific joints and intervals of the gait cycle that are related to impairments after stroke (also called subtasks) [ 10 , 11 , 13 , 14 ].…”

Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders

“…In addition, it should be investigated whether the AT algorithm itself can be further improved. To promote active participation of the patient, our AT algorithm decreases the assistance when errors are small, however, it is not known yet whether adding a forgetting factor [14,16] leads to even more active participation of the patient. It might also be beneficial to automatically tune other parameters (e.g.…”

“…To objectively and quickly tune the robotic assistance and to promote active participation of the patient, various algorithms that automatically adjust the amount of robotic assistance for lower limbs [11][12][13][14][15][16][17][18][19][20][21] or upper limbs [22][23][24][25][26] have been developed. Some of these algorithms gradually adapt the assistance based on an error compared to a reference trajectory and a forgetting factor [13,14,16,21]. Others use reference trajectories (e.g.…”

Processing Surface EMG Signals for Exoskeleton Motion Control

“…Impedance control mechanisms (Wen et al, 2011) adjust the robotic stiffness, actuator force and position to get a designated interaction. This impedance control has been used in the Lokomat robot for rehabilitation training among patients (Jezernik et al, 2004;Maggioni et al, 2018), but the impedance parameters need to be manually adjusted for different patients making it difficult to select the appropriate impedance parameters (Riener et al, 2005). These researches demonstrated a trend that neurorehabilitation technologies have been directed toward creating robotic exoskeletons to restore motor function in impaired individuals.…”