Wang Ke, Liu Yong, Huang Chengwei
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
Front Neurorobot. 2023 Jul 14;17:1219170. doi: 10.3389/fnbot.2023.1219170. eCollection 2023.
This article presents a cross-domain robot (CDR) that experiences drive efficiency degradation when operating on water surfaces, similar to drive faults. Moreover, the CDR mathematical model has uncertain parameters and non-negligible water resistance. To solve these problems, a radial basis function neural network (RBFNN)-based active fault-tolerant control (AFTC) algorithm is proposed for the robot both on land and water surfaces. The proposed algorithm consists of a fast non-singular terminal sliding mode controller (NTSMC) and an RBFNN. The RBFNN is used to estimate the impact of drive faults, water resistance, and model parameter uncertainty on the robot and the output value compensates the controller. Additionally, an anti-input saturation control algorithm is designed to prevent driver saturation. To optimize the controller parameters, a human decision search algorithm (HDSA) is proposed, which mimics the decision-making process of a crowd. Simulation results demonstrate the effectiveness of the proposed control methods.
本文提出了一种跨域机器人（CDR），该机器人在水面上运行时会出现驱动效率下降的情况，类似于驱动故障。此外，CDR数学模型具有不确定参数和不可忽略的水阻力。为了解决这些问题，针对该机器人在陆地和水面上的运行，提出了一种基于径向基函数神经网络（RBFNN）的主动容错控制（AFTC）算法。所提出的算法由快速非奇异终端滑模控制器（NTSMC）和RBFNN组成。RBFNN用于估计驱动故障、水阻力和模型参数不确定性对机器人的影响，其输出值对控制器进行补偿。此外，设计了一种抗输入饱和控制算法以防止驱动器饱和。为了优化控制器参数，提出了一种模拟人群决策过程的人工决策搜索算法（HDSA）。仿真结果证明了所提出控制方法的有效性。
