ℒ₁ – ℬℒ Adaptive Controller Design for Wrist Rehabilitation Robot

Abstract

Upgrading control algorithms to maximize the benefit of robot rehabilitation and increase the patient’s active participation in treatment is very important. The ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> adaptive controller is able to meet this need properly while performing the movement. In this study, ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> Bayesian learning adaptive control (ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -ℬℒAC) method is proposed to maximize rehabilitation in the wrist robot. In this method, unlike the ℒ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> adaptive control method, there is no need to convert the nonlinear system to a semi-linear system and it estimates this nonlinear term (which is considered as uncertainty) by using Bayesian learning algorithm (ℬℒ) along with control signal decomposition. Finally, in order to evaluate the proposed method, the results of its simulation on the wrist robot are analyzed and compared with the results of the ${\mathcal{L}_1}{\text{ - }}\mathcal{G}\mathcal{P}\mathcal{R}$ adaptive control method.