Abstract
This paper describes a Field-programmable Gate Array (FPGA) implementation of Adaptive Neuro-fuzzy Inferences Systems (ANFIS) using Very High-Speed Integrated Circuit Hardware-Description Language (VHDL) for controlling temperature and humidity inside a tomato greenhouse. The main advantages of using the HDL approach are rapid prototyping and allowing usage of powerful synthesis controller through the use of the VHDL code. The use of hardware description language (HDL) in the application is suitable for implementation into an Application Specific Integrated Circuit (ASIC) and Field tools such as Quartus II 8.1. A set of six inputs meteorological and control actuators parameters that have a major impact on the greenhouse climate was chosen to represent the growing process of tomato plants. In this contribution, we discussed the construction of an ANFIS system that seeks to provide a linguistic model for the estimation of greenhouse climate from the meteorological data and control actuators during 48 days of seedlings growth embedded in the trained neural network and optimized using the backpropagation and the least square algorithm with 500 iterations. The simulation results have shown the efficiency of the implemented controller.
Highlights
Under greenhouse production, the climate control is a tool used for yield crop manipulation that maximizes the entrepreneurial benefits
In order to reduce the number of pins used in Field-programmable Gate Array (FPGA) we have made a de-multiplexing as shown in “Fig. 6”, it has one input of 8-bits and three selection lines, in order to learn at each clock pulse one input and he settles it into a buffer
“Fig. 9”, shows the global simulation timing obtained by Quartus II version 8.1 SJ Web edition
Summary
The climate control is a tool used for yield crop manipulation that maximizes the entrepreneurial benefits. Researchers have used many control techniques in different fields, from the conventional or classic strategies [proportional integral derivative (PID) control, cascade], artificial intelligence (AI) (fuzzy control, neural networks and genetic algorithms), advanced control techniques (predictive control, adaptive), to robust control strategies, non-linear and optimal control They have been applied in the area of greenhouse climate control [1][2][3]. The greenhouse must create the favorable conditions of the plants growth, but it must be able to ensure certain flexibility in the calendar of production: precocity and spreading out of the calendar To carry out this objective a robust model using the Artificial Neural Networks and the fuzzy logic can be well adapted to control the nonlinear comportment of greenhouse climate accurately is more than necessary [5]. Outputs of this layer are called normalized firing strengths
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