帕金森状态的慢变量反馈模糊控制
摘要
Closed-loop deep brain stimulation is an effective method for controlling the Parkinsonian state. However, the twin issues of how to obtain a suitable feedback variable and design a high-performance control strategy are still unresolved. This paper proposes a variable universe fuzzy closed-loop control method based on slow variable to modulate the abnormal Parkinsonian state. For highly nonlinear neural systems, in order to achieve energy optimization of the control signal, this paper designs a closed-loop control strategy of thalamic neurons by combining unscented Kalman filter with variable universe fuzzy control, with the objective of improving the firing patterns of thalamic neurons via external stimuli with lower energy consumption. Using a slow variable as the feedback variable significantly decreases the fluctuations and energy expenditure of the stimuli. Qualitative and quantitative analyses conducted demonstrate that the proposed variable universe fuzzy closed-loop control strategy based on slow variable is effective.