RBF neural network-based admittance PD control for knee rehabilitation robot

摘要

Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions.Duringthese sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at thisstage, 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 aclinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirablemovements. The developed system has an innovative mechanism to measure interaction torques exerted byinvoluntary movements. Then, we introduce a new control approach consisting of an admittance controller and aproportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBFguides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torquesand updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirablemovement 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, anadmittance adaptive sliding mode control and an admittance conventional PDone. Then, a compatarive study is conductedon these controllers via real-world experiments. The obtained results verify the efficiency of the admittancePD-RBF and prove its superiority over the other aforementioned controllers.