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arXiv 2212.03225math.OCcs.SYeess.SY
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非线性系统鲁棒局部稳定化:基于输入输出采样的凸方法

Robust Local Stabilization of Nonlinear Systems with Controller-Dependent Norm Bounds: A Convex Approach with Input-Output Sampling

Sze Kwan Cheah, Diganta Bhattacharjee, Maziar S. Hemati, Ryan J. Caverly

AI总结:

本文提出一种基于输入输出采样的凸方法,用于非线性系统鲁棒局部稳定化,通过三次半定规划迭代算法实现控制器合成。

摘要:

本文提出了一种框架,用于合成具有未知非线性性的系统的鲁棒全状态反馈控制器。我们的方法利用可用的采样数据,以局部范数界的形式表征非线性行为,对应于已知的平衡点附近区域。该方法的一个挑战是,如果非线性具有显式依赖于控制输入的特性,则需要在控制器合成之前先选择控制输入采样区域以确定局部范数界。这导致了一个“鸡与蛋”的问题,即局部范数界用于控制器合成,但需要表征的控制输入区域在控制器合成之前无法确定。为了解决这个问题,在合成控制器时将闭环控制输入限制在采样区域内。由于所得到的合成问题是非凸的,通过主问题的凸松弛得到三个半定规划(SDPs),并使用这些SDPs构建迭代算法进行控制合成。两个数值示例用于展示所提算法的有效性。

英文摘要:

This letter presents a framework for synthesizing a robust full-state feedback controller for systems with unknown nonlinearities. Our approach characterizes input-output behavior of the nonlinearities in terms of local norm bounds using available sampled data corresponding to a known region about an equilibrium point. A challenge in this approach is that if the nonlinearities have explicit dependence on the control inputs, an a priori selection of the control input sampling region is required to determine the local norm bounds. This leads to a "chicken and egg" problem, where the local norm bounds are required for controller synthesis, but the region of control inputs needed to be characterized cannot be known prior to synthesis of the controller. To tackle this issue, we constrain the closed-loop control inputs within the sampling region while synthesizing the controller. As the resulting synthesis problem is non-convex, three semi-definite programs (SDPs) are obtained through convex relaxations of the main problem, and an iterative algorithm is constructed using these SDPs for control synthesis. Two numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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