论文标题
特征值映射基于广义超级扭曲算法的离散化
Eigenvalue Mapping-based Discretization of the Generalized Super-Twisting Algorithm
论文作者
论文摘要
在本文中，采用基于特征值映射的离散化方法来离散广义的超级扭转算法。现有的特征值映射扩展到复杂域，该域极大地扩大了参数选择的范围。此外，我们提出了找到新的特征值映射功能（EMFS）的线索。本文提出了一个新的混合动力电动势和三个全新的EMF。与常规方法相反，提出的离散方法完全避免了离散化的chat不休，并且控制精度会根据稳态误差而增强。此外，控制精度对控制收益的高估不敏感，这使控制器在实践中受益。模拟示例验证了提出的离散方法的有效性和优势。
In this paper, an eigenvalue mapping-based discretization method is applied to discretize the generalized super-twisting algorithm. The existing eigenvalue mapping is extended to the complex domain which greatly enlarges the range of parameter selection. Furthermore, we present the clue to find new eigenvalue mapping functions (EMFs). One new hybrid EMF and three brand-new EMFs are proposed in this paper. In contrast to the conventional methods, the proposed discretization method totally avoids the discretization chattering and the control precision is enhanced in terms of the steady-state error. Besides, the control precision is insensitive to the overestimation of the control gains, which benefits the gain tuning of the controller in practice. Simulation examples verify the effectiveness and superiority of the proposed discretization methodology.
