IEEE Trans Neural Netw Learn Syst. 2022 Nov;33(11):6545-6557. doi: 10.1109/TNNLS.2021.3082407. Epub 2022 Oct 27.
A novel robust adaptive neural network (NN) control scheme with prescribed performance is developed for the 3-D trajectory tracking of underactuated autonomous underwater vehicles (AUVs) with uncertain dynamics and unknown disturbances using new prescribed performance functions, an additional term, the radial basis function (RBF) NN, and the command-filtered backstepping approach. Different from the traditional prescribed performance functions, the new prescribed performance functions are innovatively proposed such that the time desired for the trajectory tracking errors of AUVs to reach and stay within the prescribed error tolerance band can be preset exactly and flexibly. The additional term with the Nussbaum function is designed to deal with the underactuation problem of AUVs. By means of RBF NN, the uncertain item lumped by the uncertain dynamics of AUVs and unknown disturbances is eventually transformed into a linearly parametric form with only a single unknown parameter. The developed control scheme ensures that all signals in the AUV 3-D trajectory tracking closed-loop control system are bounded. Simulation results with comparisons show the validity and the superiority of our developed control scheme.
针对具有不确定动力学和未知干扰的欠驱动自治水下机器人（AUV）的 3-D 轨迹跟踪问题，提出了一种新的鲁棒自适应神经网络（NN）控制方案，具有规定性能。该方案使用新的规定性能函数、附加项、径向基函数（RBF）NN 和命令滤波反推方法。与传统的规定性能函数不同，新的规定性能函数创新性地提出，使得 AUV 的轨迹跟踪误差达到和保持在规定的误差容限带内所需的时间可以精确和灵活地预设。附加项与 Nussbaum 函数一起设计，用于处理 AUV 的欠驱动问题。通过 RBF NN，最终将 AUV 的不确定动力学和未知干扰所集中的不确定项转化为仅具有单个未知参数的线性参数形式。所开发的控制方案确保 AUV 3-D 轨迹跟踪闭环控制系统中的所有信号都是有界的。与比较结果的仿真表明了我们所开发的控制方案的有效性和优越性。
