###### Abstract  
As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer's active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer's leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient. exoskeleton; rehabilitation training; adaptive sliding mode control; variable admittance control +
Footnote †: journal: Appliedmore on patients' active participation in rehabilitation training [5] and safety and comfort during rehabilitation training [6]. For patients with lower limb paralysis, because the lower limb muscles do not actively contract [7], in order to prevent lower limb muscle atrophy, the affected limb is passively driven by the exoskeleton, and the purpose of limb movement recovery is gradually achieved under passive rehabilitation training. In the existing literature, various LLREs that can be used for passive rehabilitation training have been developed and studied, such as MINDWALKER [8] (University of Twente, Enschede, Netherlands), ATLAS [9] (Centre for Automation and Robotics, La Poveda, Madrid, Spain), ReWalk [10] (Rewalk Robotics, Marlborough, MA, USA), EKSO [11] (Ekso Bionics, Richmond, VA, USA), Twin [12] (IIT, Via Morego, Genova, Italy), etc. The control system of the above LLREs is essentially a passive position control system, because active participation of the wearer is not considered in the system. However, for patients with lower limb dysfunction, such as locomotive syndrome and sarcopenia, they still have a certain amount of muscle strength retained in their affected limbs [13]. The patient's active movement intention should be taken into consideration in rehabilitation training to improve active participation, thereby improving the effect of rehabilitation training [14]. In the study of LLRE control strategies for the rehabilitation training of patients with residual muscle strength in their affected limbs, two main issues need attention. The first is how exoskeleton adaptively adjusts training tasks according to the condition of different patients' affected lower limbs [15; 16; 17]. The second is how to optimize the exoskeleton control strategy based on the active muscle force of the patients' lower limbs, so that they can participate in rehabilitation training as much as possible to achieve maximum exercise of the muscles. The LLRE robot can directly interact with patients' lower limbs [18; 19]. On one hand, LLRE needs to provide power for lower limbs to follow the standard gait trajectory [20]. On the other hand, it is necessary to ensure that the power provided can be adaptively adjusted according to the patient's active muscle force to ensure active participation [21].