Posts Tagged Time delay estimation

[Abstract + References] Cartesian Sliding Mode Control of an Upper Extremity Exoskeleton Robot for Rehabilitation

Abstract

Rehabilitation robots play an important role in rehabilitation treatment. Unlike conventional rehabilitation approach, the rehabilitation robotics provides an intensive rehabilitation motion with different modes (passive, active and active-assisted) based on the ability of the exoskeleton robot to perform assistive motion for a long period. However, this technology is still an emerging field. In this chapter, we present a Cartesian adaptive control based on a robust proportional sliding mode combined with time delay estimation for controlling a redundant exoskeleton robot called ETS-MARSE subject to uncertain nonlinear dynamics and external forces. The main objective of this research is to allow the exoskeleton robot to perform both rehabilitation modes, passive and active assistive motions with real subjects. The stability of the closed loop system is solved systematically, ensuring asymptotic convergence of the output tracking errors. Experimental results confirm the efficiency of the proposed control to provide an excellent performance despite the presence of dynamic uncertainties and external disturbances.

References

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[Abstract + References] Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties

Summary

This paper investigates the passive and active control strategies to provide a physical assistance and rehabilitation by a 7-DOF exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user’s physiological characteristics. An Integral backstepping controller incorporated with Time Delay Estimation (BITDE) is used, which permits the exoskeleton robot to achieve the desired performance of working under the mentioned uncertainties constraints. Time Delay Estimation (TDE) is employed to estimate the nonlinear uncertain dynamics of the robot and the unknown disturbances. To overcome the limitation of the time delay error inherent of the TDE approach, a recursive algorithm is used to further reduce its effect. The integral action is employed to decrease the impact of the unmodeled dynamics. Besides, the Damped Least Square method is introduced to estimate the desired movement intention of the subject with the objective to provide active rehabilitation. The controller scheme is to ensure that the robot system performs passive and active rehabilitation exercises with a high level of tracking accuracy and robustness, despite the unknown dynamics of the exoskeleton robot and the presence of unknown bounded disturbances. The design, stability, and convergence analysis are formulated and proven based on the Lyapunov–Krasovskii functional theory. Experimental results with healthy subjects, using a virtual environment, show the feasibility, and ease of implementation of the control scheme. Its robustness and flexibility to deal with parameter variations due to the unknown external disturbances are also shown.

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