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System identification

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


Papers
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Journal ArticleDOI
TL;DR: A heuristic algorithm is proposed for constructing effective Pareto optimal sensor configurations that are superior, in terms of computational efficiency and accuracy, to the Pare to sensor configurations predicted by evolutionary algorithms suitable for solving general multi-objective optimisation problems.

102 citations

Journal ArticleDOI
TL;DR: A brief outline is given of mainstream system identifications, i.e. the typical approaches, algorithms, and properties in the world of data-based model construction, to move from parameter estimation to system identification.
Abstract: A brief outline is given of mainstream system identifications, i.e. the typical approaches, algorithms, and properties in the world of data-based model construction. A number of important problems that are not sufficiently understood are pointed out. Particular attention is given to the problem of how to develop constructive and systematic ways to determine suitable model structures, i.e. to move from parameter estimation to system identification. >

102 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a nonlinear actuator-fault detection and isolation system, which properly works over the entire operating envelope of an aircraft and is capable of handling two simultaneous actuator failures with no increase of the computational load.
Abstract: In this paper, three main limitations of the classical implementation of the multiple-model adaptive-estimation method to isolate faults based on predefined fault hypotheses are highlighted. The first concerns the number of filters that must be designed to span the range of possible fault scenarios, which must be limited due to computational load. The second limitation appears when an actuator is locked at an arbitrary nonzero position that biases the residuals of the Kalman filters, leading to inaccurate fault detection and state estimation. Third, most of the implementations of a multiple-model adaptive-estimation method only work efficiently around predefined operating conditions. This paper presents a nonlinear actuator-fault detection and isolation system, which properly works over the entire operating envelope of an aircraft. Locked-in-place and floating actuator faults can be handled. The robustness of the fault detection and isolation system is enhanced by the usage of auxiliary excitation signals. The fault detection and isolation system is also capable of handling two simultaneous actuator failures with no increase of the computational load. The complete system was demonstrated in simulation with a nonlinear model of a model aircraft in moderate to severe wind conditions.

101 citations

Journal ArticleDOI
01 Sep 1997-Robotica
TL;DR: This paper describes the experimental evaluation of three identification schemes to determine the dynamic parameters of a two degrees of freedom direct-drive robot, based on the filtered dynamic regression model, the supplied energy regression model and a new one proposed: the filtered power regression model.
Abstract: This paper describes the experimental evaluation of three identification schemes to determine the dynamic parameters of a two degrees of freedom direct-drive robot. These schemes involve a recursive estimator while the regression models are formulated in continuous time. The fact that the total energy of robot manipulators can be represented as a linear relation in the inertial parameters, has motivated the suggestion in the literature of several regression models which are linear in a common dynamic parameter vector. Among them, in this paper we consider the schemes based on the filtered dynamic regression model, the supplied energy regression model and a new one proposed in this paper: the filtered power regression model. The underling recursive parameter estimator used in the experimental evaluation is the standard least-squares.

101 citations

Journal ArticleDOI
Ling Xu1, Feng Ding1
TL;DR: The impulse signal is an instant change signal in very short time, and since the cost function is highly nonlinear, the nonlinear optimization methods are adopted to derive the parameter estimation algorithms to enhance the estimation accuracy.
Abstract: The impulse signal is an instant change signal in very short time. It is widely used in signal processing, electronic technique, communication and system identification. This paper considers the parameter estimation problems for dynamical systems by means of the impulse response measurement data. Since the cost function is highly nonlinear, the nonlinear optimization methods are adopted to derive the parameter estimation algorithms to enhance the estimation accuracy. By using the iterative scheme, the Newton iterative algorithm and the gradient iterative algorithm are proposed for estimating the parameters of dynamical systems. Also, a damping factor is introduced to improve the algorithm stability. Finally, using simulation examples, this paper analyzes and compares the merit and weakness of the proposed algorithms.

101 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023177
2022361
2021646
2020813
2019804
2018862