One of the challenging problems in real-time control of movement disorders is the effective handling of time-variant brain activities that involve stochastic functional networks with nonlinear dynamics. For such challenges in neuromodulation tasks, fuzzy logic control (FLC) has shown significant potential. The objective of this paper is to present a FLC-based strategy to treat pathological symptoms of movement-disorder with higher performance. The strategy is two-fold: first, develop a design methodology for the FLC system that can robustly control pathological conditions and significantly improve control performance; and second, develop a hardware-efficient implementation for real-time neuromodulation applications. To enhance control performance, a hidden variable in the neural network that can be estimated using an unscented Kalman filter is identified as a feedback variable. In comparison with state-of-the-art schemes, the proposed design can adaptively optimize the control signals without requiring particular information of the controlled plant, thus avoiding repeated determinations of controller parameters. A field-programmable gate array is used for the reconfigurable realization of the entire control strategy based on a modification of the original neural network. The presented design, with enhanced control performance and higher hardware efficiency, has significant potential for clinical treatment of movement disorders and offers a new perspective on applications in the fields of neural control engineering and brain–machine interfaces.

中文翻译：

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基于隐属性的运动障碍变论域模糊控制设计及其高效可重构实现


运动障碍实时控制的挑战性问题之一是有效处理涉及具有非线性动力学的随机功能网络的时变大脑活动。对于神经调节任务中的此类挑战，模糊逻辑控制（FLC）已显示出巨大的潜力。本文的目的是提出一种基于 FLC 的策略，以更高的性能治疗运动障碍的病理症状。该策略有两个方面：首先，开发一种FLC系统的设计方法，可以稳健地控制病理条件并显着提高控制性能；其次，为实时神经调节应用开发硬件高效的实现。为了增强控制性能，可以使用无迹卡尔曼滤波器估计的神经网络中的隐藏变量被识别为反馈变量。与最先进的方案相比，所提出的设计可以自适应地优化控制信号，而不需要受控对象的特定信息，从而避免了控制器参数的重复确定。在对原始神经网络进行修改的基础上，采用现场可编程门阵列来可重构地实现整个控制策略。该设计具有增强的控制性能和更高的硬件效率，在运动障碍的临床治疗方面具有巨大的潜力，并为神经控制工程和脑机接口领域的应用提供了新的视角。