Deshpande Sunil, Rivera Daniel E
Control Systems Engineering Laboratory (CSEL), Arizona State University, Tempe, AZ, USA. Doctoral student in the electrical engineering program at Arizona State.
Proc Am Control Conf. 2013:3924-3929. doi: 10.1109/acc.2013.6580439.
Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design.
以数据为中心的估计方法，如按需建模和直接权重优化，是从噪声数据中估计未知函数的有吸引力的技术。这些方法依赖于在当前工作点从回归器数据库生成局部函数近似，并在每个新的工作点重复该过程。本文研究了为产生供局部建模程序使用的信息性数据而制定的最优输入信号的设计。所提出的方法专门解决回归器向量的分布问题。针对输入受幅度约束的线性时不变系统进行了设计研究。使用半定松弛方法解决由此产生的优化问题。数值例子表明了与经典伪随机二进制序列（PRBS）输入设计相比的优势。
