发表机构
UNAM（墨西哥国立自治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于滑模控制的模型化扰动抑制方法，通过内模原理和隐式离散化实现有限时间收敛，达到$T^{p+1}\ar D$的控制精度，仿真验证其有效性。
AI 中文摘要
我们提出了一种用于参考跟踪的鲁棒精确滑模控制器，该控制器具有基于模型的扰动抑制功能，其中扰动由受有界输入驱动的线性时不变模型生成，并且可能与受控对象双向耦合。该控制器嵌入了内模原理，其误差动态与鲁棒精确微分器的误差动态相似，确保了有限时间收敛。我们开发了一种恰当的隐式离散化方法，用于采样数据实现，该方法保持了稳定性和准确性。在标称情况下，其中包含由$p$阶积分器链生成的扰动，该方法实现了$T^{p+1}\ar D$的控制精度，其中$T$为离散化时间，$\ar D$为扰动界。该精度被证明与基本下界相关，其因子仅取决于扰动模型的阶数。仿真验证了所提出方法的有效性。
英文摘要
We propose a robust exact sliding-mode controller for reference tracking with model-based disturbance rejection, where the disturbance is generated by a linear time-invariant model driven by a bounded input and may be bidirectionally coupled with the plant. The controller embeds the internal model principle and its error dynamics mirror those of the robust exact differentiator, ensuring finite-time convergence. We develop a proper implicit discretization for sampled-data implementation that preserves stability and accuracy. In the nominal case, which contains a disturbance generated by a $p$-th order integrator chain, the method achieves a control accuracy of $T^{p+1}\bar D$, with discretization time $T$ and disturbance bound $\bar D$. This accuracy is shown to relate to the fundamental lower bound by a factor depending only on the order of the disturbance model. Simulations demonstrate the efficacy of the proposed methods.
Commentspreprint submitted to Automatica
