Fuzzy Logic, Identification and Predictive Control

Espinosa, Jairo; Vandewalle, Joos; Wertz, Vincent

doi:10.1007/b138626

Cited by 112 publications

(65 citation statements)

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“…Fuzzy Inferance System (FIS) in fuzzy logic can be used to model a system which is an inference system based on empirical knowledge based linguistic rules [13]. The fuzzy inference system is composed of five steps: fuzzification of the input variables, application of the fuzzy operator (AND or OR) in the precedent, implication from the precedent to the subsequent, aggregation of the subsequents across the rules, and defuzzification.…”

Section: Anfis Modelingmentioning

confidence: 99%

“…Figure 5 shows the plot of Gaussian membership functions for fuzzification of sEMG. The process of fuzzification followed by IF .. AND.. THEN ..OR ELSE type control or relation/estimation rules [13]. The output of the rules is computed as a max-min composition [13].…”

Section: Anfis Modelingmentioning

confidence: 99%

“…The fuzzy inference system is composed of five steps: fuzzification of the input variables, application of the fuzzy operator (AND or OR) in the precedent, implication from the precedent to the subsequent, aggregation of the subsequents across the rules, and defuzzification. In the fuzzification process, the membership values coming from quantitative measurements are converted to linguistic values based on the membership functions [13]. Figure 5 shows the plot of Gaussian membership functions for fuzzification of sEMG.…”

Section: Anfis Modelingmentioning

confidence: 99%

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An adaptive hybrid data fusion based identification of skeletal muscle force with ANFIS and smoothing spline curve fitting

Kumar

Chen

Sebastian

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Precise and effective prosthetic control is important for its applicability. Two desired objectives of the prosthetic control are finger position and force control. Variation in skeletal muscle force results in corresponding change of surface electromyographic (sEMG) signals. sEMG signals generated by skeletal muscles are temporal and spatially distributed that result in cross talk between adjacent sEMG signal sensors. To address this issue, an array of nine sEMG sensors is used with a force sensing resistor to capture muscle dynamics in terms of sEMG and skeletal muscle force. sEMG and skeletal muscle force are filtered with a nonlinear Teager-Kaiser Energy (TKE) operator based nonlinear spatial filter and Chebyshev type-II filter respectively. Multiple Takagi-Sugeno-Kang Adaptive Neuro Fuzzy Inference Systems (ANFIS) are obtained using sEMG as input and skeletal muscle force as output. Outputs of these ANFIS systems are fitted with smoothing spline curve fitting. To achieve better estimate of the skeletal muscle force, an adaptive probabilistic Kullback Information Criterion (KIC) for model selection based data fusion algorithm is applied to the smoothing spline curve fitting outputs. Final fusion based output of this approach results in improved skeletal muscle force estimates.

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Section: Anfis Modelingmentioning

confidence: 99%

Section: Anfis Modelingmentioning

confidence: 99%

Section: Anfis Modelingmentioning

confidence: 99%

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An adaptive hybrid data fusion based identification of skeletal muscle force with ANFIS and smoothing spline curve fitting

Kumar

Chen

Sebastian

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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“…They included the Multi-layer perceptron (MLP), neural network (NN) [25,31], Radial-basis-function (RBF) NN [31,32], support vector machine regression (SVM) [33,34], and Pace regression (Pacereg) [24,35]. The MLP NN and RBF NN algorithms are usually used in non-linear regression and classification modeling due to their ability to capture complex relationships between parameters.…”

Section: Algorithm Selection For Iaq Sensor Modelingmentioning

confidence: 99%

“…The sensitivity of a neural network's output to its input perturbation is an important issue in theory and practice. The sensitivity analysis [31,32] indicates input variables that are most important for a particular neural network. It often identifies variables that can be safely ignored in subsequent analyses and key variables that must always be retained.…”

Section: Validation Of Iaq Models Based On a 2-week Data Setmentioning

confidence: 99%

Virtual models of indoor-air-quality sensors

Kusiak

Zheng

2010

Applied Energy

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Fuzzy 12 sectors improved direct torque control of a DFIG with stator power factor control strategy

Ayrir

Haddi

2019

Int Trans Electr Energ Syst

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Summary In this paper, an improved direct torque control (DTC) strategy based on a fuzzy inference system is proposed to control a doubly fed induction generator (DFIG)‐based wind energy conversion system (WECS). The proposed method is based on increasing the number of sectors to 12 instead of six in the classical DTC (C‐DTC), and the use of a fuzzy inference system to generate the optimal voltage vector to apply to the rotor side converter (RSC). The proposed method aims to reduce the flux and torque undulations which are considered the main disadvantage of the C‐DTC and to control the power factor at the stator terminals by generating the referential rotor flux from the referential reactive power. The effectiveness of the proposed strategy is well tested by the simulation. Moreover, a total harmonic distortion (THD) analysis was performed for the rotor and stator currents waveforms. The results show a considerable reduction of the undulations for both flux and torque, and a significant reduction of the THD.

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Fuzzy Logic, Identification and Predictive Control

Cited by 112 publications

References 0 publications

An adaptive hybrid data fusion based identification of skeletal muscle force with ANFIS and smoothing spline curve fitting

An adaptive hybrid data fusion based identification of skeletal muscle force with ANFIS and smoothing spline curve fitting

Virtual models of indoor-air-quality sensors

Fuzzy 12 sectors improved direct torque control of a DFIG with stator power factor control strategy

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