Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

中文翻译：

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基于磁滞神经直接逆补偿器的磁滞非线性系统的自适应神经控制及其应用


针对一类磁滞非线性系统的高精度控制问题，该文设计了一种新的基于磁滞直接逆补偿器的自适应输出反馈控制方案。首先，建立了一种新颖的基于长短期记忆神经网络 （LSTMNN） 的磁滞逆补偿器来补偿非对称磁滞非线性，其中 LSTMNN 用作模型算子权重的预测机制，而不是磁滞输入和输出的整体映射。其次，通过设计改进的高增益 K-Filter 状态观测器和误差变换函数，以任意小的估计误差估计不可测态，实现预定的跟踪性能。最后，构建了双锥形介电弹性体致动器 （DEA） 运动平台。然后，在实验平台上验证了所提出的基于 LSTMNN 的磁滞逆补偿器和控制方案的有效性。实验结果表明了所提控制方案的有效性和优势。