Manual rigid endoscopes have defects such as a low efficiency, difficult operation, and safety risks, and the antinoise interference ability, convergence speed, and control accuracy of the neural network control technology for the existing autonomous endoscopes are often ignored. Solving these problems is important for the stable operation of endoscopes. Therefore, a new adaptive fast convergent antinoise dual neural network (AFA-DNN) controller for the visual servo control of ten-degree of freedom flexible endoscope robots (FERs) with physical constraints is proposed in this work. First, the control scheme of the FERs is formulated as a quadratic programming problem, and then, an AFA-DNN visual servo controller is designed for the FERs. The adaptive gains of the controller can accelerate the convergence, improve the antinoise ability, and increase the convergence accuracy of the controller. Then, according to the Lyapunov theory, the fast convergence of the AFA-DNN in finite time is proven for both noise-free and noisy conditions. The experimental results indicate that the FER controlled by the proposed AFA-DNN can accurately track various trajectories and that the AFA-DNN has a better antinoise interference ability, higher convergence accuracy, and faster convergence speed than conventional methods. The convergence speed of the AFA-DNN is increased by a factor of 4.22 by using the adaptive gains. Experiments also indicate that the AFA-DNN remains well functioning under various noise disturbances (such as constant, periodic, linear, and Gaussian noise).

中文翻译：

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适用于柔性内窥镜机器人的具有自适应增益的快速收敛抗噪声双神经网络控制器


手动硬性内窥镜存在效率低、操作困难、存在安全风险等缺陷，而现有自主内窥镜的神经网络控制技术的抗噪声干扰能力、收敛速度和控制精度往往被忽视。解决这些问题对于内窥镜的稳定运行具有重要意义。因此，本文提出了一种新的自适应快速收敛抗噪声双神经网络（AFA-DNN）控制器，用于具有物理约束的十自由度柔性内窥镜机器人（FER）的视觉伺服控制。首先，将 FER 的控制方案表述为二次规划问题，然后，为 FER 设计 AFA-DNN 视觉伺服控制器。控制器的自适应增益可以加速收敛，提高抗噪声能力，提高控制器的收敛精度。然后，根据 Lyapunov 理论，在无噪声和噪声条件下都证明了 AFA-DNN 在有限时间内的快速收敛。实验结果表明，所提出的AFA-DNN控制的FER能够准确跟踪各种轨迹，并且AFA-DNN比传统方法具有更好的抗噪声干扰能力、更高的收敛精度和更快的收敛速度。通过使用自适应增益，AFA-DNN 的收敛速度提高了 4.22 倍。实验还表明，AFA-DNN 在各种噪声干扰（例如恒定噪声、周期性噪声、线性噪声和高斯噪声）下仍然保持良好的功能。