Nonlinear System Identification with Neurofuzzy Methods

Nelles, Oliver

doi:10.1007/978-1-4615-6191-0_12

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…1. The output perturbation can be given in terms of the system input as in (2), and the prediction error for the indirect identification of the system can be given in terms of the closed-loop modelsĜ C andĤ C = 1 by:…”

Section: A Prediction Error Methods (Pem) Frameworkmentioning

confidence: 99%

“…This renders the following proposition: Proposition 1: In local identification of a nonlinear feedback system with an integral action using a linear model structure, bias removal is unnecessary. 2 This proposition has two implications: First, for controller synthesis, techniques based on PL models can be utilized, and extension to piecewise affine models is unnecessary. Second, according to [13], [14], in feedback nonlinear systems with integral actions, the necessary and sufficient conditions for existence of a family of linear controllers (designed to stabilize the individual linearized models of the system) that stabilize the nonlinear system are readily satisfied.…”

Section: B Model Structure Selection: Linear Vs Affinementioning

confidence: 99%

“…In systems with hysteresis or stiction nonlinearities, measuring α is nontrivial2 For certain classes of nonlinear systems such as those with hysteresis extra conditions might be necessary.…”

mentioning

confidence: 98%

“…[1], or through a concurrent identification of linear models from global data, e.g. [2].In this paper Takagi-Sugeno (TS) control of the elevation channel of a twin-rotor system, as an unstable nonlinear SISO system, based on closed-loop I/O data is considered. Applications of different control schemes such as fuzzy sliding [3], model predictive [4], decoupling control [5], and evolutionary PID control [6] to twin-rotor systems have been reported in the literature.…”

mentioning

confidence: 99%

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Takagi-Sugeno control of the elevation channel of a twin-rotor system using closed-loop empirical data

Azimian

Fatehi

Araabi

2012

2012 American Control Conference (ACC)

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In this paper, design of Takagi-Sugeno controllers for a twin-rotor system from empirical data is addressed. First, identification of linear models using Prediction Error Method in the presence of nonlinear distortions and in feedback systems is revisited, and it is demonstrated that feedback nonlinear systems with an integral action can be locally represented by linear models rather than affine models. Moreover, it is demonstrated that how the consistency and unbiasedness of the identified models can be improved by proper design of the experiments. In order to ensure that the unmodeled contributions in the resulting piecewise linear model are sufficiently small, a criterion is proposed based on the small-gain theorem that can be used for unfalsification of the piecewise linear model. Finally, by taking advantage of the separation principle, design of fuzzy Takag-Sugeno state feedback and observer is formulated in terms of Linear Matrix Inequalities (LMIs). The implementation results on the actual system demonstrate significant improvements with respect to the initial controller and previous work on similar systems. I. INTRODUCTIONControl of nonlinear systems based on a family of linear models has been studied widely for decades, and several theoretical frameworks have been developed. In these frameworks, it has been presumed that a mathematical model of the nonlinear system is available, and local models can be generated by linearization of the system. In practice, particularly for more complicated systems, a control engineer may not have access to such models and, alternatively, identification of linear models from input/output (I/O) data must be considered. A piecewise linear (PL) model can be constructed from a family of local linear models of the system, e.g. [1], or through a concurrent identification of linear models from global data, e.g. [2].In this paper Takagi-Sugeno (TS) control of the elevation channel of a twin-rotor system, as an unstable nonlinear SISO system, based on closed-loop I/O data is considered. Applications of different control schemes such as fuzzy sliding [3], model predictive [4], decoupling control [5], and evolutionary PID control [6] to twin-rotor systems have been reported in the literature. Herein, given a low quality controller with an integral action, provided by the manufacturer, a family of linear models are identified at various operating

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Section: A Prediction Error Methods (Pem) Frameworkmentioning

confidence: 99%

Section: B Model Structure Selection: Linear Vs Affinementioning

confidence: 99%

mentioning

confidence: 98%

mentioning

confidence: 99%

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Takagi-Sugeno control of the elevation channel of a twin-rotor system using closed-loop empirical data

Azimian

Fatehi

Araabi

2012

2012 American Control Conference (ACC)

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“…It implements a heuristic search for the rule premise parameters and avoids a time consuming nonlinear optimization. The LOLIMOT algorithm is described in five steps according to (Nelles, 1999(Nelles, , 2001):…”

Section: Locally Linear Neurofuzzy With Model Tree Learningmentioning

confidence: 99%

Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling

2006

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Even one-step prediction of natural time series without delay especially in main phase of storm is difficult for many complicated time series such as Dst index. In this study, with a new method based on singular spectrum analysis, we extract the main components of the time series, model each component with a locally linear neurofuzzy network, and utilize the trained networks for multi-step ahead prediction of a validation set of data, and finally combine the predicted patterns for construction of general prediction. Our methods are compared with several previous studies for Dst index prediction. Several solar geomagnetic extreme events are predicted well with our state-of-the-art method; such as extreme events in 14 March 1989 that led to power black-out in Quebec, as well as other extreme storms.

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Dynamic Local Linear Neuro-Fuzzy Models

Nelles¹

2020

Nonlinear System Identification

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Nonlinear System Identification with Neurofuzzy Methods

Cited by 7 publications

References 20 publications

Takagi-Sugeno control of the elevation channel of a twin-rotor system using closed-loop empirical data

Takagi-Sugeno control of the elevation channel of a twin-rotor system using closed-loop empirical data

Multi-step prediction of Dst index using singular spectrum analysis and locally linear neurofuzzy modeling

Dynamic Local Linear Neuro-Fuzzy Models

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