用自适应过程减小参数不确定性的影响，并通过动态滑模控制器的高鲁棒性抑制机器人模型中的未知非线性动力学及测量噪声等的影响。
The adaptive process extenuates the influence of parameter uncertainty, and the robustness of the dynamic sliding mode control inhibits the influence of unknown dynamics and measurement noise.
该方法综合了自适应模糊系统的逼近能力和滑模控制鲁棒性强的优点。
This method integrates the merits of approximate ability of adaptive fuzzy system and robustness of the sliding control.
针对非完整移动机器人的轨迹跟踪控制问题，提出了一种鲁棒项系数自调整的神经网络滑模自适应控制策略。
An adaptive neural sliding mode control strategy with the self-tuning of robust item coefficients is proposed for the trajectory tracking of non-holonomic wheeled mobile robots.
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