Undesired lateral and longitudinal wheel slippage can disrupt a mobile robot's heading angle, traction, and, eventually, desired motion. This issue makes the robotization and accurate modeling of heavy-duty machinery very challenging because the application primarily involves off-road terrains, which are susceptible to uneven motion and severe slippage. As a step toward robotization in skid-steering heavy-duty robot (SSHDR), this paper aims to design an innovative robust model-free control system developed by neural networks to strongly stabilize the robot dynamics in the presence of a broad range of potential wheel slippages. Before the control design, the dynamics of the SSHDR are first investigated by mathematically incorporating slippage effects, assuming that all functional modeling terms of the system are unknown to the control system. Then, a novel tracking control framework to guarantee global exponential stability of the SSHDR is designed as follows: 1) the unknown modeling of wheel dynamics is approximated using radial basis function neural networks (RBFNNs); and 2) a new adaptive law is proposed to compensate for slippage effects and tune the weights of the RBFNNs online during execution. Simulation and experimental results verify the proposed tracking control performance of a 4,836 kg SSHDR operating on slippery terrain.

翻译：不良的横向与纵向车轮滑移会破坏移动机器人的航向角、牵引力乃至预期运动。这一问题使得重型机械的机器人化与精确建模极具挑战性，因为其应用场景主要涉及非结构化地形，此类地形易引发运动失稳与严重滑移。作为滑移转向重型机器人（SSHDR）机器人化进程中的一步，本文旨在设计一种由神经网络构建的创新鲁棒无模型控制系统，以在广泛潜在车轮滑移存在的情况下强稳定机器人动力学。在控制设计前，首先通过数学方法融合滑移效应对SSHDR动力学进行建模分析，并假设系统的所有功能建模项对控制系统均未知。随后，设计了一种保证SSHDR全局指数稳定性的新型轨迹跟踪控制框架，其构建如下：1）利用径向基函数神经网络（RBFNNs）逼近未知的车轮动力学模型；2）提出一种新型自适应律，用于在线补偿滑移效应并实时调整RBFNNs的权重。仿真与实验结果验证了所提轨迹控制方法在4,836公斤级SSHDR于滑移地形运行时的控制性能。