A genetically trained simplified ANFIS Controller to control nonlinear MIMO systems

Lutfy, Omar Farouq; Noor, Samsul Bahari Mohd; Marhaban, Mohammad Hamiruce

doi:10.1109/inecce.2011.5953905

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Layer 3 employs the product t-norm as the aggregation operator, and it typically consists of K fuzzy neurons. Each node is associated with a specific rule, and the output of each neuron determines the degree to which the jth rule is being followed [21,22].…”

Section: Implementation Of Dpfc Integrated To Genetic-anfismentioning

confidence: 99%

Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

2023

IRJMETS

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The increasing global demand for electricity has driven the exploration of renewable energy sources to generate power in an environmentally friendly manner. Solar photovoltaic systems and wind-based generators are considered significant sources of renewable energy and are referred to as Distributed Generation units due to their dispersed nature. These units use bidirectional converters to provide auxiliary services on both the grid side and the load side, regardless of the microgrid operation mode. Additionally, DC power generation units are converted into AC systems through inverters. These systems not only introduce voltage and current harmonics and power frequency deviations but also push the distribution system into precarious operating conditions. This underscores the need for advanced control strategies for microgrid architecture.As a result, power electronic converters introduce harmonics into the system, which can impact overall system performance. To address these emerging challenges, the authors have introduced an advanced custom power device called the Distributed Power Flow Controller (DPFC). In this study, the proposed solar-wind hybrid energy system is primarily evaluated using the DPFC mechanism. Subsequently, the system is further assessed using a Genetic Algorithm (GA)-based fuzzy logic controller and a GA-based Adaptive Neuro Fuzzy Inference System (ANFIS) controller for shunt control within the context of the DPFC mechanism. The validity of the results is confirmed through simulations using MATLAB/Simulink software.

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Section: Implementation Of Dpfc Integrated To Genetic-anfismentioning

confidence: 99%

Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

2023

IRJMETS

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“…ANFIS combines a neural network with fuzzy logic and thus achieves a learning mechanism for a fuzzy rule base. It is widely regarded as an universal estimator [17].…”

Section: Learning Controller From Training Datamentioning

confidence: 99%

Real time fuzzy controller for quadrotor stability control

Bhatkhande

Havens

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM Cortex M4 based controller board. xv We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

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Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

2018

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A genetically trained simplified ANFIS Controller to control nonlinear MIMO systems

Cited by 8 publications

References 11 publications

Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

Power Quality Enhancement of Solarwind Grid Connected System Employing Genetic-Based Anfis Controller

Real time fuzzy controller for quadrotor stability control

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

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