This research introduces a new exoskeleton-type rehabilitation robot, which can be used in lower limb rehabilitation therapy for post-stroke patients. A novel design of a typical knee and ankle rehabilitation robot is proposed. The kinematic and dynamic models of the knee and ankle rehabilitation robot are derived. Furthermore, a super-twisting nonsingular terminal sliding mode control is developed to achieve the desired training missions and its results are compared with those of an adaptive sliding mode control. To reduce undesired interaction torques between knee and ankle rehabilitation robot and patient, an admittance control algorithm is added to the controller to guarantee a safe therapy session. The admittance super-twisting nonsingular terminal sliding mode control structure is considered as the novelty of this article. Taking into account the dynamic uncertainties, external disturbances, and the interaction torques, the validity of the admittance super-twisting nonsingular terminal sliding mode control controller is approved by various numerical simulations over the admittance adaptive sliding mode control.

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Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.