A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications

Er, Meng Joo; Mandal, Sayantan

doi:10.1109/tfuzz.2015.2501439

Cited by 27 publications

(4 citation statements)

References 132 publications

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“…Figure 4 shows the outlay of this paper's neuro-fuzzy scheme. It comprises four layers: Input layer, membership layer, rule layer, and the output layers [32,33].…”

Section: Principles Of Adaptive Neuro-fuzzy Controlmentioning

confidence: 99%

“…in this case is a single node that computes results as in Equation (18), where w ij , µ ij , and x i are as earlier defined, N r is the total number of rules, and N mf is the total number of membership functions [32,33]:…”

Section: The Output Layermentioning

confidence: 99%

“…The data were then used to train the initial membership functions created (see Figure 7a). Details rules regarding selections of appropriate membership are outside the scope of this paper, but can be found in [33]. The initial membership functions were designed with MATLAB Simulink Fuzzy Logic Toolbox by expert knowledge of the system, in such a manner that they model the relationship between input and output signals (here, the input signals are v and i , and the output signals are ∆L and ∆C).…”

Section: Neuro-fuzzy Parameter Estimationmentioning

confidence: 99%

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Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters

et al. 2021

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Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

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“…Figure 4 shows the outlay of this paper's neuro-fuzzy scheme. It comprises four layers: Input layer, membership layer, rule layer, and the output layers [32,33].…”

Section: Principles Of Adaptive Neuro-fuzzy Controlmentioning

confidence: 99%

Section: The Output Layermentioning

confidence: 99%

Section: Neuro-fuzzy Parameter Estimationmentioning

confidence: 99%

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Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters

et al. 2021

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“…In order to approximate the uncertainty of the system, a radical based saturated input is applied through designing and auxiliary system. In [4], controlling the fuzzy logic as a design view point is developed as a free control model. A new robust control is suggested for mechanical manipulator through adaptive uncertainty estimator based on Taylor irst order series.…”

Section: Introductionmentioning

confidence: 99%

One DOF Robot Manipulator Control Through Type-2 Fuzzy Robust Adaptive Controller

Naderolasli

Chatraei

2019

JAMRIS

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In this article, the one DOF robot manipulator control is assessed through second type robust fuzzy-adaptive controller. The objective is to obtain a tracking path with appropriate accuracy. The stability of the closed loop system is verified through Lyapunov stability theory and the efficiency of tracking is analyzed subject to the constraints and uncertainty. In order to design the fuzzy controller a set of if-then fuzzy rules are considered which describe the system input-output behavior. Simulation and the results of the experiments on the one DOF robots indicate the effectiveness of the proposed methods.

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