This paper presents a robust neural network with an extended state observer control methodology for a piezoelectric-actuator-based surgical device. This control methodology is proposed for tracking of desired motion trajectories in the presence of unknown or uncertain system parameters, nonlinearities including friction, hysteresis, and disturbances in the motion system. In particular, the radial basis function neural network, which serves as a function approximator, aims to create a model for an unknown function to find a relationship between input and output data. An extended state observer is utilized to assist in canceling disturbances and uncertainties of the system dynamically. The stability of the control approach is analyzed. The convergence of position and velocity tracking errors is proven theoretically. Experiments are conducted to demonstrate that the performance with an improved accuracy can be attained by the proposed control scheme. With the motion tracking capability, the control methodology helps the novel surgical device achieve higher success rate in operation, which is also suitable for similar piezoelectric ultrasonic actuator applications.

中文翻译：

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使用神经网络和扩展状态观察器对基于压电致动器的手术装置进行运动控制

本文提出了一种鲁棒的神经网络，它具有用于基于压电致动器的手术设备的扩展状态观察器控制方法。这种控制方法被提议用于在存在未知或不确定的系统参数、非线性包括摩擦、滞后和运动系统干扰的情况下跟踪所需的运动轨迹。特别是，作为函数逼近器的径向基函数神经网络旨在为未知函数创建模型，以找到输入和输出数据之间的关系。扩展状态观测器用于帮助动态消除系统的干扰和不确定性。分析了控制方法的稳定性。理论上证明了位置和速度跟踪误差的收敛性。进行实验以证明所提出的控制方案可以获得具有改进精度的性能。凭借运动跟踪能力，该控制方法有助于新型手术装置在手术中获得更高的成功率，这也适用于类似的压电超声致动器应用。