Hardware-in-the-loop simulation and implementation of a fuzzy logic controller with FPGA: case study of a magnetic levitation system

2018 ◽  
Vol 41 (8) ◽  
pp. 2150-2159 ◽  
Author(s):  
Onur Akbatı ◽  
Hatice Didem Üzgün ◽  
Sirin Akkaya
Keyword(s):  
Fuzzy Logic ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Order System ◽  
Membership Functions ◽  
Inference System ◽  
Levitation System ◽  
Magnetic Levitation System

This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.

Design of PSO-based fuzzy logic controller for single axis magnetic levitation system

2011 ◽  
Vol 6 (6) ◽  
pp. 577-584 ◽  
Author(s):  
Basheer Noaman Hussein ◽  
Nasri Sulaiman ◽  
R. K. Raja Ahmad ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Levitation System ◽  
Magnetic Levitation System

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

scholarly journals Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System

2019 ◽  
Vol 9 (12) ◽  
pp. 2458 ◽  
Author(s):  
Gabriel García-Gutiérrez ◽  
Diego Arcos-Aviles ◽  
Enrique V. Carrera ◽  
Francesc Guinjoan ◽  
Emilia Motoasca ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Rule Base ◽  
Linguistic Rules ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Nature Inspired Algorithms

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.

scholarly journals Robust Fuzzy Logic Stabilization with Disturbance Elimination

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Kumeresan A. Danapalasingam
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Control Law ◽  
Dynamic Feedback ◽  
Model Uncertainties ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Mathematical Derivation

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

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