Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: The identification of linear parameter-varying systems in an input-output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise.
148 citations
01 Dec 2003
TL;DR: It is demonstrated that experimental design significantly improves the parameter estimation accuracy and also reveals difficulties in parameter estimation due to robustness.
Abstract: To obtain a systems-level understanding of a biological system, the authors conducted quantitative dynamic experiments from which the system structure and the parameters have to be deduced. Since biological systems have to cope with different environmental conditions, certain properties are often robust with respect to variations in some of the parameters. Hence, it is important to use optimal experimental design considerations in advance of the experiments to improve the information content of the measurements. Using the MAP-Kinase pathway as an example, the authors present a simulation study investigating the application of different optimality criteria. It is demonstrated that experimental design significantly improves the parameter estimation accuracy and also reveals difficulties in parameter estimation due to robustness.
148 citations
12 Dec 2005
TL;DR: The paper reviews the emergence of this subject as a specific topic over the last 15 years, at the boundary between system identification and robust control, and shows how the early focus on identification of control-oriented nominal models has progressively shifted towards the design ofcontrol-oriented uncertainty sets.
Abstract: This paper presents the author’s views on the development of identification for control. The paper reviews the emergence of this subject as a specific topic over the last 15 years, at the boundary between system identification and robust control. It shows how the early focus on identification of control-oriented nominal models has progressively shifted towards the design of control-oriented uncertainty sets. This recent trend has given rise to an important revival of interest in experiment design issues in system identification. Some recent results on experiment design are presented.
148 citations
23 May 2004
TL;DR: In this article, a nonlinear subspace identification method has been proposed to identify a Wiener model in a format suitable for its use in a standard linear-model-based predictive control scheme.
Abstract: Wiener model identification and predictive control of a pH neutralisation process is presented. Input-output data from a nonlinear, first principles simulation model of the pH neutralisation process are used for subspace-based identification of a black-box Wiener-type model. The proposed nonlinear subspace identification method has the advantage of delivering a Wiener model in a format which is suitable for its use in a standard linear-model-based predictive control scheme. The identified Wiener model is used as the internal model in a model predictive controller (MPC) which is used to control the nonlinear white-box simulation model. To account for the unmeasurable disturbance, a nonlinear observer is proposed. The performance of the Wiener model predictive control (WMPC) is compared with that of a linear MPC, and with a more traditional feedback control, namely a PID control. Simulation results show that the WMPC outperforms the linear MPC and the PID controllers.
147 citations
TL;DR: In this article, a time domain non-parametric method for non-linear vibration system identification based on the Hilbert transform is introduced, which is demonstrated using computer simulations of different types of nonlinear elastic and damping dynamic systems.
146 citations