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An explicit optimal input design for first order systems identification

机译:一阶系统辨识的显式最佳输入设计

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This paper focuses on the problem of closed loop online identification of the time constant in the single input single output (SISO) first order linear model. A new explicit approach for the simultaneous online optimal experiment design (OED) and model parameter identification is presented. Based on the observation theory and a model based predictive control (MPC) algorithm, this approach aims to solve an optimal control problem where input and output constraints may be specified. This constrained control objective aims to maximize the sensitivity of the model output with respect to the unknown model parameter (the time constant). The control law is derived explicitly offline and simple to be implemented: the input may be computed fast online while the unknown model time constant is estimated at the same time.
机译:本文着重研究单输入单输出(SISO)一阶线性模型中时间常数的闭环在线识别问题。提出了一种同时在线优化实验设计(OED)和模型参数辨识的显式方法。基于观察理论和基于模型的预测控制(MPC)算法,此方法旨在解决可能指定输入和输出约束的最佳控制问题。该受约束的控制目标旨在使模型输出相对于未知模型参数(时间常数)的灵敏度最大化。该控制法则明确地离线导出并且易于实现:输入可以快速在线计算,而同时估计未知模型时间常数。

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