System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.

Structural-System Identification. I: Theory

Abstract: The investigation reported in this paper looks into the application of a number of system-identification techniques to problems of earthquake engineering. A number of techniques for structural-system identification have been developed over the past few years. Many of these techniques have been successful at identifying properties of linearized and time-invariant equivalent structural systems. Most of these techniques were verified using mathematical models simulated on the computer. In this paper, a number of structural-identification algorithms are reviewed and applied to the identification of structural systems subjected to earthquake excitations. The algorithms are applied to experimental data obtained in controlled laboratory conditions. The data pertain to the acceleration records from two building models subjected to various loading conditions. The performance of the various identification algorithms is critically assessed, and guidelines are obtained regarding their suitability to various engineeri...

System identification of power converters with digital control through cross-correlation methods

Abstract: For digitally controlled switching power converters, on-line system identification can be used to assess the system dynamic responses and stability margins. This paper presents a modified correlation method for system identification of power converters with digital control. By injecting a multiperiod pseudo random binary signal (PRBS) to the control input of a power converter, the system frequency response can be derived by cross-correlation of the input signal and the sensed output signal. Compared to the conventional cross-correlation method, averaging the cross-correlation over multiple periods of the injected PRBS can significantly improve the identification results in the presence of PRBS-induced artifacts, switching and quantization noises. An experimental digitally controlled forward converter with an FPGA-based controller is used to demonstrate accurate and effective identification of the converter control-to-output response.

Lazy learning for local modelling and control design

Abstract: This paper presents local methods for modelling and control of discrete-time unknown non-linear dynamical systems, when only input-output data are available. We propose the adoption of lazy learning, a memory-based technique for local modelling. The modelling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. A lso, three methods to design controllers based on the local linearization provided by the lazy learning algorithm are described. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired by self-tuning regulators where recursive least squares estimation is replaced by a local approximator. The third method combines the linearization provided by t...