Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

Rigatos, Gerasimos; Tzafestas, Spyros G.

doi:10.1080/01443610500212468

Cited by 266 publications

(116 citation statements)

References 11 publications

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“…The observer's gain is computed through the Kalman Filter's recursion [29][30][31]. To apply Kalman Filtering on the linearized equivalent model of the system, which is also known as Derivative-free nonlinear Kalman Filter, matrices A o , B o , C o are subjected to discretization with the use of common discretization methods.…”

Section: Disturbances Compensation With the Derivativefree Nonlinear mentioning

confidence: 99%

“…To estimate the non-measurable state variables of the system the Derivative-free nonlinear Kalman Filter is proposed [19][20][21]. This consists of the Kalman Filter recursion applied on the equivalent linearized model of the system [29][30][31]. Moreover, it makes use of an inverse transformation which is based on differential flatness theory and which allows to obtain estimates of the state variables of the initial nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

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Control of DC–DC Converter and DC Motor Dynamics Using Differential Flatness Theory

et al. 2016

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The article proposes a method for nonlinear control of the dynamical system that is formed by a DC-DC converter and a DC motor, making use of differential flatness theory. First it is proven that the aforementioned system is differentially flat which means that all its state vector elements and its control inputs can be expressed as differential functions of primary state variables which are defined to be the system's flat outputs. By exploiting the differential flatness properties of the model its transformation to a linearized canonical (Brunovsky) form becomes possible. For the latter description of the system one can design a stabilizing feedback controller. Moreover, estimation of the nonmeasurable state vector elements of the system is achieved by applying a new nonlinear Filtering method which is known as Derivative-free nonlinear Kalman Filter. This filter consists of the Kalman Filter recursion applied on the linearized equivalent model of the system and of an inverse transformation that is based on differential flatness theory and which enables to obtain estimates of the initial nonlinear state-space model. Moreover, to compensate for parametric uncertainties and external perturbations the filter is redesigned as a disturbance observer. By estimating the perturbation inputs that affect the joint model of the DC-DC converter and of the DC motor their compensation becomes possible. The efficiency of the proposed control scheme is further confirmed through simulation experiments.

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Section: Disturbances Compensation With the Derivativefree Nonlinear mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control of DC–DC Converter and DC Motor Dynamics Using Differential Flatness Theory

et al. 2016

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“…Next, a derivative-free nonlinear Kalman Filter can be designed for the aforementioned representation of the system dynamics (Rigatos 2011(Rigatos , 2012a. The associated Kalman Filter-based disturbance estimator is given by the recursion (Rigatos and Tzafestas 2007;Rigatos and Zhang 2009) measurement update: …”

Section: State and Disturbances Estimation With The Derivativefree Nomentioning

confidence: 99%

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

Rigatos

2014

Cogn Neurodyn

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A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.

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“…In this estimation problem the process and measurement noise covariance matrices are denoted as Q(k) and R(k) respectively, while the estimation error's covariance matrix is denoted as P(k). The disturbance estimator's gain is computed with the use of the Kalman Filter recursion [26][27][28].…”

Section: Flatness-based Control Of the Nonlinear Fuel Cells Dynamicsmentioning

confidence: 99%

A PEM Fuel Cells Control Approach Based on Differential Flatness Theory

et al. 2016

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The article presents an approach to nonlinear control of fuel cells using differential flatness theory and Kalman filtering. First, it is proven that the dynamic model of fuel cells is a differentially flat one which means that all its state variables and control inputs can be expressed as differential functions of specific stare variables which are the so-called flat outputs of the system. By exploiting the differential flatness properties of the model its transformation to an equivalent linear form (canonical Brunovsky form) becomes possible. For the latter description of the system's dynamics the design of a state-feedback controller is achieved. This control scheme should be also robust to model uncertainties and external perturbations. To cope with this problem the state-space description of the PEM fuel cells is extended by considering as additional state variables the derivatives of the aggregate disturbance input. Next, a Kalman filter-based disturbance observer is applied to the linearized extended model of the fuel cells. This estimation method enables to identify the disturbance and model uncertainty terms that affect the system and to introduce a complementary control element that compensates for the perturbations' effects. The efficiency of the proposed control scheme is evaluated through simulation experiments.B G. Rigatos

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Extended Kalman filtering for fuzzy modelling and multi-sensor fusion

Cited by 266 publications

References 11 publications

Control of DC–DC Converter and DC Motor Dynamics Using Differential Flatness Theory

Control of DC–DC Converter and DC Motor Dynamics Using Differential Flatness Theory

Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter

A PEM Fuel Cells Control Approach Based on Differential Flatness Theory

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