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: In this paper, an identification method for industrial robots that does not require the a priori identification of the friction model is described, which is based on separating the base parameters into three different groups.
99 citations
TL;DR: A refined forward regression orthogonal (RFRO) algorithm is developed that cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algori...
Abstract: The minimal model structure detection (MMSD ) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidate terms would involve 2.43 1018 possible paths, it is shown that this can typically be reduced to 2 103 by augmenting the orthogonal estimation algorithm with genetic search procedures. The MMSD algorithm provides the first practical solution for optimal structure detection in NARMAX modelling, training neural networks and fuzzy systems modelling. Based on the MMSD algorithm, a refined forward regression orthogonal (RFRO ) algorithm is developed. The RFRO algorithm initially detects a parsimonious model structure using the forward regression orthogonal algorithm and then refines the model structure by applying the MMSD algorithm to the reduced model term set. The RFRO algorithm cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algori...
99 citations
TL;DR: It is proved that ANN models are able to approximate every time-dependent model described by ODEs with any desired level of accuracy, and is tested on different problems, including the model reduction of two large-scale models.
99 citations
TL;DR: In this paper, the authors present the progress that has been accomplished in iterative process control design over the last decade, illustrated with some applications in the chemical industry, where the controller parameters are iteratively tuned on the basis of successive experiments performed on the real plant.
99 citations
TL;DR: Best linear time-invariant approximations are analysed for several interesting classes of discrete nonlinear time-Invariant systems, including nonlinear finite impulse response systems and a class of nonsmooth systems called bi-gain systems.
99 citations