Abstract
In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.
Highlights
Every year, millions of people suffer from motor impairments around the world due to Spinal Cord Injury (SCI), Stroke, and Cerebral Palsy
The PD controller tries to control the user joints over a reference trajectory, in our method the PD gains are sufficiently low to allow the user to have voluntary motions around the reference trajectory and most of the task is performed by adaptation of assistive torque which assists as needed, i.e., our controller is a type of Assist as Needed (AAN)
In order to study the performance of the proposed adaptation method to optimize exoskeleton contribution gain and to ensure the stability of closed-loop system, we utilized two different simulation models: (1) a 2-DOF manipulator system which is a representation of simplified human-arm performing task in sagittal plane; (2) a detailed dynamic walking model of the human lower limb with 9-DOF; see Figure 2
Summary
Millions of people suffer from motor impairments around the world due to Spinal Cord Injury (SCI), Stroke, and Cerebral Palsy. The main assumption behind reference trajectory adaptation based on interaction force minimization is that the user can generate cyclic and reliable patterns This assumption is not valid for individuals with motor impairment and, we cannot fully rely on their exhibited trajectory and trajectory adaptation by means of interaction force minimization as it might results in a failure; this is already addressed in [19] where even changes in the control structure cannot improve the performance of trajectory adaptation. From another perspective, the AAN controller can be realized by the minimization of exoskeleton actuator torque while tracking error is reduced; it forces the exoskeleton to only compensate for tracking error reduction and prevents any further contribution. The PD controller tries to control the user joints over a reference trajectory, in our method the PD gains are sufficiently low to allow the user to have voluntary motions around the reference trajectory and most of the task is performed by adaptation of assistive torque which assists as needed, i.e., our controller is a type of AAN
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