论文标题
非线性模型的仿射线性参数嵌入具有提高的精度和最小的过量
Affine Linear Parameter-Varying Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding
论文作者
论文摘要
在本文中，考虑了线性参数变化（LPV）状态空间模型以嵌入非线性系统的动力学行为，重点是调整调度复杂性和模型准确性之间的权衡以及最小化所得嵌入的保守性。 LPV状态空间模型与仿射计划依赖关系合成，而调度变量本身是状态的非线性函数和原始系统的输入变量。该方法允许生成非线性系统模型的完整或近似嵌入，也可以用于最大程度地减少现有LPV嵌入的复杂性。该方法的能力在模拟示例和经验案例研究中证明了，其中3-DOF控制力矩陀螺仪的第一原则运动模型通过提出的方法转换为具有较低调度复杂性的LPV模型。使用所得模型，设计和应用于陀螺仪上的增益制定控制器，以证明开发方法的效率。
In this paper, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behavior of nonlinear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and on the minimization of the conservativeness of the resulting embedding. The LPV state-space model is synthesized with affine scheduling dependency, while the scheduling variables themselves are nonlinear functions of the state and input variables of the original system. The method allows to generate complete or approximative embedding of the nonlinear system model and also it can be used to minimize complexity of existing LPV embeddings. The capabilities of the method are demonstrated on simulation examples and also in an empirical case study where the first-principle motion model of a 3-DOF control moment gyroscope is converted by the proposed method to LPV model with low scheduling complexity. Using the resulting model, a gain-scheduled controller is designed and applied on the gyroscope, demonstrating the efficiency of the developed approach.
