双优拉闭环辨识方法的简化
On simplification of Dual-Youla approach for closed-loop identification
摘要：
闭环辨识的对偶尤拉法具有几个重要的实际优点。也就是说，它提供了一个准确的植物模型，不考虑噪声模型，并适合固有地处理不稳定的植物，通过互质分解。此外，该方法对控制器知识的不确定性具有经验鲁棒性。然而，互质分解的使用可能会对工业应用造成很大的障碍。本文展示了如何得到一个简化版本的方法，确定植物本身没有互质因式分解，同时享受所有的优点，双尤拉方法。这种简化的方法与作者最近提出的稳定预报误差法是一致的。详细的仿真结果证明了上述优点。
英文摘要：
The dual Youla method for closed loop identification is known to have several practically important merits. Namely, it provides an accurate plant model irrespective of noise models, and fits inherently to handle unstable plants by using coprime factorization. In addition, the method is empirically robust against the uncertainty of the controller knowledge. However, use of coprime factorization may cause a big barrier against industrial applications. This paper shows how to derive a simplified version of the method which identifies the plant itself without coprime factorization, while enjoying all the merits of the dual Youla method. This simplified version turns out to be identical to the stabilized prediction error method which was proposed by the authors recently. Detailed simulation results are given to demonstrate the above merits.
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