Robust adaptive sliding mode control for a human-driven knee joint orthosis

Abstract

Purpose Assistive technology products are designed to provide additional accessibility to individuals who have physical or cognitive difficulties, impairments and disabilities. The purpose of this paper is to deal with the control of a knee joint orthosis intended to be used for rehabilitation and assistive purpose; this control aims to reduce the influence of the uncertainties and eliminating the external disturbances in the system. Design/methodology/approach This paper deals with the robust adaptive sliding mode controller (ASMC) of human-driven knee joint orthosis system with mismatched uncertainties and external disturbances. The shank-orthosis system has been modeled and its parameters have been identified. This control reduces the effect of parameter uncertainties and external disturbances on the system performance and improves the system robustness as results. The ASMC was designed to offer the possibility to track the state of the reference model. Moreover, the Lyapunov stability theory was used to study the asymptotical stability of the ASMC. Findings The advantage of the robust ASMC method is the tracking precision and reducing the required time for eliminating external disturbances and uncertainties. The experimental results show in real-time in terms of stability and present that the advantages of this control approach are the position tracking and robustness. Originality/value In this paper, to deal with the parameter uncertainties of the human-driven knee joint orthosis, an ASMC was successfully applied based on sliding mode and Lyapunov stability theory. It has good dynamic response and tracking performance. Besides, the adaptive algorithm is simple, easy to achieve and has good adaptability and robustness against the parameter variations and external disturbances. The design technique is simple and efficient. The development of this control takes into consideration the perturbation, allowing to track a desired trajectory.