FPGA fuzzy logic controller for variable speed generators

Cirstea, Marcian; Khor, J.G.; McCormick, M.

doi:10.1109/cca.2001.973881

Cited by 21 publications

(11 citation statements)

References 3 publications

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“…Although the servo case is relatively slow, all these defuzzification methods can be analyzed and compared. Several studies have been implemented in FPGA hardware for T1FLS [16] and IT2FLS [17]. However, no comparisons have been presented, in hardware terms, where the most important defuzzification methods are applied for solving the same problem.…”

Section: Methodsmentioning

confidence: 98%

Real-Time Fuzzy Logic Controllers

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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Nonlinear control involves a nonlinear relationship between the controller's inputs and outputs and is more complicated than linear control; however, it is able to achieve better performance than linear control for many real-world control applications. Nonlinear control theory requires more challenging mathematical analysis and design than does linear control theory. An FLC is a nonlinear controller, that is, the function f(x) is nonlinear. What distinguishes an FLC, T1 or T2, from other nonlinear controllers is that it generates its nonlinear mapping function f(x) through linguistic if-then rules and linguistic terms for the antecedents and consequents of the rules (e.g., Low Speed, High Speed). Such rules can be (easily) obtained from a human operator or can be postulated and learned from data. According to Kosko [1], an FLC is unique in that it ties vague words like Low and High, and common sense rules, to state-space geometry [2]. Fuzzy logic controllers have two important advantages over other classes of nonlinear controllers.

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Section: Methodsmentioning

confidence: 98%

Real-Time Fuzzy Logic Controllers

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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“…Particle Swarm Optimization (PSO) is an optimization technique based on swarm intelligence [2], it originates from studying on the predatory behavior of the bird. it is an iterative random search algorithm in nature with parallel processing features, robustness [3].…”

Section: Introductionmentioning

confidence: 99%

Fractional Order PID Controller Based on Particle Swarm Optimization Implemented with FPGA

Huang

2010

2010 International Conference on Artificial Intelligence and Computational Intelligence

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According to parameters adjustment problems of fractional order PID controller applied in time-varying、 nonlinear 、 real-time demanding higher systems, an improved particle swarm optimization (PSO) algorithm is adopted to optimize fractional order PID controller parameters. Because of high complexity of PSO and long tuning time, the paper adopts FPGA and combined with external circuit to implement fractional order PID controller. The results show that the improved PSO improves the parameters precision of the fractional order PID controller and adaptive capacity of control systems, and the real-time of systems is well met.

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“…It means we need to low that displacement parameter as early as possible that will give the better ride comfort and the road handling have been improved. In real world, construction of mathematical models (white-box models)it is very tricky because we seldom to have a complete knowledge of the process that we require to model [6].some fractional order PID controller will be used to deal with this mathematical model parameter [1].An (EA)Evolutionary algorithm has been used to serve for this purpose.…”

Section: (3)mentioning

confidence: 99%

“…Reference [5] there will be adaptive generalized predictive control method for nonlinear system for more than one order that was many input and many output and established the stability and convergence of blocked loop system. In this [6] obstacle avoidance there was program for mobile robot that thesaurus a neuro fuzzy algorithm using altera field programmable gate array development path. FPGA development and the implementing board were used _______________________________________________________________________________________ to be a successful system platform for the NF(neuro-fuzzy) algorithm to be implemented and tested .…”

Section: Introductionmentioning

confidence: 99%

“…Reference [5] in this it will be implemented a fuzzy controller(FC) on an FPGA and also it will be presented using VHDL for a motor unit. In Reference [6] simulation and implementation of a logic controller were using the fuzzy system for a diesel determined impartial synchronous generator have been designed. The developing the controller using hardware descriptive language and implemented in FPGA.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of ANFIS Algorithm Using VHDL for Vechicular System

Wadalkar¹

2015

IJRITCC

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Abstract:-In this paper an (Adaptive Neuro Fuzzy(NF) Inference System algorithm)ANFISA proposed and try to implemented using FPGA(FIELD PROGRAMMABLE GATE ARRAY)for the behavior of the system will be nonlinear. In the application of designing the algorithm for vehicular controlling of nonlinear active suspension system. The algorithm for neural was Back propagation(BPN) and for fuzzy takagi-sugeno-kang active suspension systems is very important for guaranteeing the passengers feel the comfortable riding and also road handled quality for a vehicle. It is shown that the ANFIS can modelize a nonlinear system very accurately by means of data taken from mathematical model. Firstly the MATLAB SIMULINK toolboxes were use for to simulate the proposed controllers in the pre view paper and to display the responses of the controlled model under different types of disturbance. But in this paper the implementation of t he (ANFIS)adaptive neuro fuzzy inference system algorithm using FPGA boards has been try to investigated in this work. The Xilinx ISE software is employed to synthesis the VHDL codes used to program the FPGA.

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FPGA fuzzy logic controller for variable speed generators

Cited by 21 publications

References 3 publications

Real-Time Fuzzy Logic Controllers

Real-Time Fuzzy Logic Controllers

Fractional Order PID Controller Based on Particle Swarm Optimization Implemented with FPGA

Design and Implementation of ANFIS Algorithm Using VHDL for Vechicular System

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