Fuzzy logic control via an FPGA: a design using techniques from digital signal processing

2004 ◽  
Author(s):  
T. Lund ◽  
M. Aguirre ◽  
A. Torrala
Keyword(s):  
Signal Processing ◽  
Fuzzy Logic ◽  
Digital Signal Processing ◽  
Fuzzy Logic Control ◽  
Digital Signal ◽  
Logic Control

Rapid Prototyping of Fuzzy Logic Controllers

1996 ◽  
Author(s):  
Kevin J. Gorman ◽  
Kourosh J. Rahnamai
Keyword(s):  
Signal Processing ◽  
Fuzzy Logic ◽  
Digital Signal Processing ◽  
Rapid Prototyping ◽  
Real Time ◽  
Digital Signal ◽  
Fuzzy Logic Controllers ◽  
Graphical Interfaces ◽  
Logic Inference ◽  
Logic Controllers

Abstract The rapid prototyping of fuzzy logic controllers is accomplished by using the tools Matlab, Simulink, Fuzzy Logic Toolkit, and Real-Time Workshop. Device drivers were developed for Simulink for interfacing with DT2801 and DT2821 data acquisition boards. The fuzzy logic inference engine for the Fuzzy Logic Toolkit was modified to allow the systems to work as independent programs and to be downloadable to DSP (Digital Signal Processing) boards. Simulink is used to graphically implement fuzzy logic controllers. The Real-Time Workshop is used to compile blocks from Simulink into C code, then into an independent executable program, both on the PC and a dSpace DSP (Digital Signal Processing) board. Graphical interfaces are created and debugged by using dSPACE’s tools, Cockpit and Trace. By combining these tools, real-time fuzzy logic controllers are developed in laboratory environments.

scholarly journals FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

2021 ◽  
Vol 10 (3) ◽  
pp. 195
Author(s):  
Emna Aridhi ◽  
Decebal Popescu ◽  
Abdelkader Mami
Keyword(s):  
Signal Processing ◽  
Fuzzy Logic ◽  
Digital Signal Processing ◽  
Ambient Temperature ◽  
Software Design ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Digital Signal ◽  
Execution Cycle ◽  
Simulation Results

This paper invests in FPGA technology to control the speed of an autonomous car using fuzzy logic. For that purpose, we propose a co-design based on a novel fuzzy controller IP. It was developed using the hardware language VHDL and driven by the Zynq processor through an SDK software design written in C. The proposed IP acts according to the ambient temperature and the presence or absence of an obstacle and its distance from the car. The partitioning of the co-design tasks divides them into hardware and software parts. The simulation results of the fuzzy IP and those of the complete co-design implementation on a Xilinx Zynq board showed the effectiveness of the proposed controller to meet the target constraints and generate suitable PWM signals. The proposed hardware architecture based on 6-LUT blocks uses 11 times fewer logic resources than other previous similar designs. Also, it can be easily updated when new constraints on the system are to be considered, which makes it suitable for many related applications. Fuzzy computing was accelerated thanks to the use of digital signal processing blocks that ensure parallel processing. Indeed, a complete execution cycle takes only 7 us.

Method of estimation of hidden correlation of narrowband noise signals

2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
New Technology ◽  
Digital Signal ◽  
Measuring Instruments ◽  
Central Frequency ◽  
Pearson Criterion ◽  
Useful Signal ◽  
Band Limited ◽  
Narrowband Noise

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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