— A large numbers of fuzzy control applications with the physical systems require a real-time operation to interface high speed constraints; higher density programmable logic devices such as field programmable gate array (FPGA) can be used to integrate large amounts of logic in a single IC. This paper reviews the state of the art of FPGA with the focus on FPGA-based fuzzy logic controller. The paper starts with an overview of FPGA in order to get an idea about FPGA architecture, and followed by an explanation on the hardware implementation with both type analogue and digital implementation, a comparison between fuzzy and conventional controller also provided in this paper. A survey on fuzzy logic controller structure is highlighted in this article with the focus on FPGA-based design of fuzzy logic controller with different applications. Finally, we provided the simulation and experimental results form the literature and concluded the main differences between software-based systems with respect to FPGA-based systems, and the main features for FPGA technology and its real-time applications.

FPGA-Based Fuzzy Logic: Design and Applications – a Review

Partial shading on photovoltaic (PV) modules reduces the generated power of the PV system than the maximum power generated from each module separately. The shaded PV module acts as a load to unshaded ones which can lead to hot-spot. To alleviate the effect of partial shading, bypass diodes should be connected across each PV modules. Connecting several PV modules together produces multiple peaks (one global peak (GP) and multiple local peaks (LPs)) on partial shading conditions. Maximum power point tracker conventional techniques are designed to follow the GP but they stuck around LPs such as fuzzy logic controller (FLC). In this paper, modified particle swarm optimization (MPSO) using genetic algorism has been used to follow the GP under any operating conditions. MPSO has been studied and compared with the FLC technique to show the superiority of this technique under all operating conditions. Co-simulation between Matlab/Simulink and PSIM has been used to model the PV system under partial shading conditions. The simulation results show that the MPSO technique is more effective than FLC in following the GP. The generated power increases considerably with the MPSO than the FLC technique in shading conditions.

Performance of smart maximum power point tracker under partial shading conditions of photovoltaic systems

The cell has optimum operating point to be able to get maximum power. To obtain Maximum Power from photovoltaic array, photovoltaic power system usually requires Maximum Power Point Tracking (MPPT) controller. This paper provides a small power photovoltaic control system based on fuzzy control with FPGA technology design and implementation for MPPT. The system composed of photovoltaic module, buck converter and the fuzzy logic controller implemented on FPGA for controlling on/off time of MOSFET switch of a buck converter. The proposed maximum power point tracking controller for photovoltaic system is tested using model designed by Matlab/Simulink program with graphical user interface (GUI) for entering the parameters of any array model using information from its datasheet, Simulation and experimental results show that performance of the fuzzy controller with FPGA in a maximum power tracking of a photovoltaic array can be made use of in several photovoltaic products and obtain satisfied result. Keywords-Fuzzy Control; MPPT; Photovoltaic System; FPGA.

/pdf/fuzzy-controller-design-using-fpga-for-photovoltaic-maximum-2n07ov9hhb.pdf

Fuzzy Controller Design Using FPGA for Photovoltaic Maximum Power Point Tracking

The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The most commonly used controller in the industry field is the proportional-plus-integral-plus-derivative (PID) controller. Fuzzy logic controller (FLC) provides an alternative to PID controller, especially when the available system models are inexact or unavailable. Also rapid advances in digital technologies have given designers the option of implementing controllers using Field Programmable Gate Array (FPGA) which depends on parallel programming. This method has many advantages over classical microprocessors. In this research, A model of the fuzzy PID control system is implemented in real time with a Xilinx FPGA (Spartan-3A, Xilinx Company, 2007). It is introduced to maintain a constant speed to when the load varies.,The model of a DC motor is considered as a second order system with load variation as a an example for complex model systems. For comparison purpose, two widely used controllers “PID and Fuzzy” have been implemented in the same FPGA card to examine the performance of the proposed system. These controllers have been tested using Matlab/Simulink program under speed and load variation conditions. The controllers were implemented to run the motor as real time application under speed and load variation conditions and showed the superiority of Fuzzy-PID.

/pdf/fuzzy-pid-controllers-using-fpga-technique-for-real-time-dc-48c0nrng21.pdf

Fuzzy PID Controllers Using FPGA Technique for Real Time DC Motor Speed Control

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements This necessitates a very fast and accurate controller to meet as much control objectives, as possible Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering A full vehicle nonlinear active suspension system is introduced and tested The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλDμ (FOPID) controller The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

/pdf/adaptive-neuro-fuzzy-inference-controller-for-full-vehicle-1gtjusec05.pdf

Adaptive Neuro Fuzzy inference controller for full vehicle nonlinear active suspension systems

In this paper, a fuzzy proportional integral derivative (PID) controller, which is fabricated on a field programmable gate array (FPGA) is proposed to control an industrial process The fuzzy logic is provided to tune the PID parameters by using a gain scheduling method The tuning scheme is represented by a fuzzy system, which consists of fuzzification rule inference and defuzzification The fabricated FPGA chip, XC2S50-5tq-144, is embedded with the fuzzy system, which the chip is called fuzzy-PID controller The fuzzy-PID controller is contained several circuits, such as multiplier, adder, subtraction and some of the other logic gate The controller system uses two converter cards: TDA8763AM/3 (10 bits, high speed) to convert from analog to digital signal DAC08 (8 bits) to convert from digital to analog The maximum speed of the controller about 2,1 /spl mu/sec per action at 40550 MHz is calculated from timing summary The fuzzy-PID controller based on FPGA is verified by using to control model of level and temperature process From the experimental results of the level and temperature control, the fuzzy-PID controller has achieved and shown the better performance than standard PID controller E50AK which is automatic tuneable parameter system

V. Krongratana

Papers

Fuzzy logic PID controller based on FPGA for process control