This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics. For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the “explosion of differentiation” caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded. Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.

中文翻译：

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基于扩展状态观测器的输入饱和柔性关节机器人系统动态表面控制

本文提出了一种基于扩展状态观察器的动态表面控制方法，用于具有非对称输入饱和度和大量未知动态知识的柔性关节机器人系统。传统的柔性关节机器人系统控制器通常使用逼近技术来处理未知的动力学知识。与传统的控制算法不同，本文利用扩展状态观察器来估计未知动态。对于闭环系统，延迟策略处理时标分离问题，滤波系统克服辅助控制信号重复微分导致的“微分爆炸”，均值定理解决输入饱和问题执行器的。稳定性分析意味着扩展状态观测器 (ESO) 和其他状态变量的估计误差是半全局一致的最终有界。与模糊控制算法相比，新的基于 ESO 的动态表面控制方法不仅省略了在线学习时间，而且仅使用少量控制参数即可获得令人满意的跟踪性能。最后，通过对比仿真实验来说明所得结论的有效性。