An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.

针对具有未知车轮滑移效应的不确定非完整移动机器人，开发了一种基于量化状态反馈的自适应跟踪设计策略。在网络控制环境中，假设所有状态变量和控制转矩分别由状态和输入量化器量化。因此，量化状态反馈信息仅可用于跟踪控制设计。递归地设计使用量化状态的基于近似的自适应控制器，以确保针对未知车轮滑移效应的鲁棒自适应跟踪，其中导出基于量化状态的自适应机制来补偿未知的车轮滑移效应、系统非线性和量化误差。通过提出一些理论引理，分析了闭环系统中量化误差和估计参数的有界性。基于这些引理，我们证明了闭环信号的一致极限有界性和存在车轮滑移效应时轨迹跟踪误差的收敛性。仿真验证了所得跟踪方案的有效性。