Regularized System Identification: The Prior Knowledge Awakens

In control engineering practice, the users of system identification may have prior knowledge of control plants to be identified. In order to get better model estimates and further improve the modeling efficiency, the users have thus been suggested to make an intelligent choice of experiment design, model set, and identification criterion guided by prior knowledge as well as by observed data. However, it is hard to incorporate prior knowledge of control plants in classical system identification paradigm and there are few results on the systematic use of prior knowledge of control plants in system identification until the birth of the so-called regularized system identification in 2010. The regularized system identification proposes to estimate the impulse response model by a regularized least squares method and its major novelty lies in that with the impulse response model, it is possible to impose an underlying model structure by a carefully designed regularization that incorporates/embeds the prior knowledge of control plants. After ten years of development, it has become a viable paradigm and research frontier of system identification. In this talk, I will share my personal view/experience on the development of the regularized system identification and show how some key problems in regularized system identification can be solved by exploring the “control” characteristics of the prior knowledge of the control plant to be identified. This leads to the view that it is important to develop the regularized system identification methods by making use of the “control” characteristics of the prior knowledge of the control plant to be identified.