System identification

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

System identification in communication with chaotic systems

Abstract: Communication using chaotic systems is considered from a control point of view It is shown that parameter identification methods may be effective in building reconstruction mechanisms, even when a synchronizing system is not available. Three worked examples show the potentials of the proposed method.

Transfer function identification in power system applications

Abstract: The authors present an introduction to concepts and applications of transfer function identification in power systems. They begin with a brief introduction to transfer function identification methods using least-squares approaches and then discuss applications which include static VAR compensators, model validation applications, and software validation. A comparison is also made between eigenvalues obtained from transfer function identification and small signal analysis. Methods for testing the validity of identified transfer functions are also discussed. >

Frequency domain identification of Hammerstein models

Abstract: Discusses Hammerstein model identification in the frequency domain using sampled input-output data. By exploring the fundamental frequency and harmonics generated by the unknown nonlinearity, we propose a frequency domain approach and show its convergence for both the linear and nonlinear subsystems in the presence of noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.

On the parameter estimation and modeling of aggregate power system loads

Abstract: This paper addressed some theoretical and practical issues relevant to the problem of power system load modeling and identification. Two identification techniques are developed in the theoretical framework of stochastic system identification. The identification techniques presented in this paper belong to the family of output error models; both techniques are based on well-established equations describing load recovery mechanisms having a commonly recognized physical appeal. Numerical experiments with artificially created data were first performed on the proposed techniques and the estimates obtained proved to be asymptotically unbiased and achieved the corresponding Crame/spl acute/r-Rao lower bound. The proposed techniques were then tested using actual field measurements taken at a paper mill, and the corresponding results were used to validate a commonly used aggregate load model. The results reported in this paper indicate that the existing load models satisfactorily describe the actual behavior of the physical load and can be reliably estimated using the identification techniques presented herein.

Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles

Abstract: The effectiveness of subsea intervention has been found to be dependent upon the capability of an autonomous underwater vehicle's (AUV's) or remotely operated underwater vehicle's (ROV's) auto-positioning system. However, these vessel's dynamics vary considerably with operating condition, and are strongly coupled; they are expensive and difficult to derive, theoretically or through conventional testing, making the design of conventional autopilots difficult to achieve. Multi-input-multi-output self-tuning controllers offer a possible solution. Two such schemes are presented. The first is an implicit linear quadratic online, self-tuning controller, and the other uses a robust control law based on a first-order approximation of the open-loop dynamics and online recursive identification. The controllers' performance is evaluated by examining their behavior when controlling a comprehensive nonlinear simulation of an ROV and its navigation system. An interesting offshoot of this study is the application of recursive system identification techniques to the derivation of ROV models from data gathered from the trials; the potential advantages of this method are discussed. >