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Analysis, estimation and controller design of parameter-dependent systems using convex optimization based on linear matrix inequalities.

机译:基于线性矩阵不等式的凸优化的参数相关系统的分析,估计和控制器设计。

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In this thesis, we outline three contributions in robust control. The first is the efficient computation of a lower bound on the robust stability margin (RSM) of uncertain systems. A lower bound on the RSM can be derived using the framework of integral quadratic constraints (IQCs). Current techniques for numerically computing this lower bound use a bisection scheme. We show how this bisection can be avoided altogether by reformulating the lower bound computation problem as a single generalized eigenvalue minimization problem, which can be solved very efficiently using standard algorithms.; For the second contribution, we focus on linear systems affected by parametric uncertainties. For these systems, we present sufficient conditions for robust stability. We also derive conditions for the existence of a robustly stabilizing gain-scheduled controller when the system has time-varying parametric uncertainties that can be measured in real time. Our approach is proven to be in general less conservative than existing methods.; Our third contribution is on the robust estimation of systems having parametric uncertainties. For systems with mixed deterministic and stochastic uncertainties, we design two optimized steady state filters: (i) the first filter minimizes an upper bound on the worst-case gain in the mean energy between the noise affecting the system and the estimation error; (ii) the second filter minimizes an upper bound on the worst-case asymptotic mean square estimation error when the plant is driven by a white noise process. For time-varying systems with stochastic uncertainties, we derive a robust adaptive Kalman filtering algorithm. This algorithm offers considerable improvement in performance when compared to the standard Kalman filtering techniques. We demonstrate the performance of these robust filters on numerical examples consisting of the design of equalizers for communication channels.
机译:在本文中,我们概述了鲁棒控制中的三个贡献。首先是不确定系统鲁棒稳定性裕度(RSM)下限的有效计算。可以使用整数二次约束(IQC)框架得出RSM的下限。用于数值计算该下限的当前技术使用对分方案。我们展示了如何通过将下限计算问题重新构造为单个广义特征值最小化问题来完全避免该二等分,这可以使用标准算法非常有效地解决。对于第二个贡献,我们关注受参数不确定性影响的线性系统。对于这些系统,我们提出了鲁棒稳定性的充分条件。当系统具有可以实时测量的时变参数不确定性时,我们还得出了存在稳定鲁棒的增益调度控制器的条件。事实证明,我们的方法通常不如现有方法保守。我们的第三点贡献是对具有参数不确定性的系统进行可靠的估计。对于具有确定性和随机不确定性混合的系统,我们设计了两个优化的稳态滤波器:(i)第一个滤波器使影响系统的噪声与估计误差之间的平均能量的最坏情况增益的上限最小化; (ii)当植物由白噪声过程驱动时,第二个滤波器使最坏情况下渐近均方估计误差的上限最小化。对于具有随机不确定性的时变系统,我们推导了一种鲁棒的自适应卡尔曼滤波算法。与标准卡尔曼滤波技术相比,该算法可显着提高性能。我们在包含通信信道均衡器设计的数值示例上演示了这些鲁棒滤波器的性能。

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