Simultaneous control of rigidity and hand tremor by adaptive fuzzy Q-learning

Deep brain stimulation (DBS) is a successful treatment for the symptoms of various neurode-generative diseases, including Parkinson's disease (PD). While DBS has been shown to improve motor symptoms in individuals with PD, it operates mostly in an open-loop mode, meaning that the stimulation settings remain constant regardless of changes in the disease state. To address this issue, the current paper describes the use of a fuzzy Q-learning algorithm combined with a model-free non-linear controller, called FQL-based MFNC, to control both hand tremor and rigidity at the same time. In contrast to previous tuning methods that focus on obtaining the mathematical model of a controlled system, the current technique explores the use of a fuzzy Q-learning agent to achieve the best design for DBS. Initially, a fuzzy reinforcement learning approach is employed to adjust the proposed controller in a simulation setting. Specifically, the FQL algorithm de-termines the best approach based on a reward function to modify the designed control co-efficients, ensuring that the DBS requirements are satisfied despite changes in tremors. Multiple examinations, including Normal, Noise, and Stability tests, along with different indices, have been conducted to validate the efficacy and feasibility of the proposed FQL-based MFNC approach on a dynamic model pertaining to rigidity and tremors.