Impedance learning adaptive super‐twisting control of a robotic exoskeleton for physical human‐robot interaction

Abstract

This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities. The first step is to estimate the human's target position (also known as TPH). The second step is to develop a robust adaptive impedance control mechanism. A novel Non‐singular Terminal Sliding Mode Control combined with an adaptive super‐twisting controller is being developed to achieve this goal. This combination's purpose is to provide high reliability, continuous performance tracking of the system's trajectories. The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering, which is the sliding mode's most glaring issue. The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network, which allows a robotic exoskeleton to simply track the desired impedance model. To validate the approach in real‐time, an exoskeleton robot was deployed in controlled experimental circumstances. A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.

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

“…Therefore, to address such perturbations, researchers have explored the adaptive control schemes for upperand lower-extremity rehabilitation devices. [16][17][18][19][20][21][22][23][24] The literature of this work is limited to adaptive control schemes of upper-extremity devices only. Khan et al 16 exploited a passivity-based adaptive controller on an EXoRD to track the estimated Desired Motion Intention (DMI) of upper-limbs.…”

“…The controller gains are adjusted using the adaptation law. Brahmi et al 21 presented the combination of non-singular terminal sliding mode control with an adaptive super-twisting scheme to estimate the matched uncertainties. A residual chattering is found to be exist at different joints of the rehabilitation robot during the assigned exercise.…”

“…Therefore, all traditional control strategies seem incompetent to deal with network-induced challenges, and some modifications are required in the controller design process. 27 The previously mentioned position and hybrid control schemes [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] exploit such time-triggered approach where the control signals are transmitted to the dynamics at each sample time irrespective of the actual demand. This increases transmission costs with ineffective resource allocation and may be impractical in scenarios with limited communication bandwidth or high-latency connections.…”

“…Therefore, for the first time, the ETAB admittance approach is proposed for the robot-assisted upper limb rehabilitation problem under the dynamic uncertainties, limited communications and interaction between subject and robot. Thirdly, compared to the adaptive sub-optimal control 20 or the adaptive super-twisting sliding mode, 21 this work proposes an adaptive backstepping control in the inner loop which involves a systematic design approach to implicitly construct the Lyapunov candidate function for non-linear systems rather than finding the same based on heuristic methods. Moreover, it does not require the input-output dynamics to be linear compared to most adaptive control approaches.…”

“…The previously mentioned position and hybrid control schemes 10–26 exploit such time-triggered approach where the control signals are transmitted to the dynamics at each sample time irrespective of the actual demand. This increases transmission costs with ineffective resource allocation and may be impractical in scenarios with limited communication bandwidth or high-latency connections.…”