Chapter Three A Proposed Design of Intelligent Controller for Solar Tracking System 3.1

2900 WordsApr 23, 201912 Pages
Chapter Three A Proposed Design of Intelligent Controller for Solar Tracking System 3.1 Introduction One of the most important methods used for obtaining the solution to analysis and design controller for sun tracking system is the computer simulation. All simulations are implemented using MATLAB R2013a and Proteus 7 Professional. 3.2 Sun Tracker Solar tracking system uses two DC motors to rotate the solar panel in two axes. The position of the sun is adjusted by using four LDR as a tracking sensor, the out from sensors are converted from analog form to digital form by using four ADC0804 ICs. The digital out from sensors will in to the intelligent controller that implemented on FPGA card and the output from the controller…show more content…
The sensor output voltage values will be input to the ADC. بعد اكو صورة للسنسرات الاربعة من يتركبن على اللوحة واشرح شلون اختاريت المسافة بين سنسر والثاني ... 3.2.2Analog to Digital Converter ADC0804 are CMOS 8-Bit, successive approximation A/D converter figure (3.5) show pins diagram for ADC0804 Figure (3.5) ADC0804 pins diagram The voltage reference input can be adjusted to allow encoding any smaller analog voltage span to the full 8 bits of resolution. Based on data sheet and Proteus 7 Professional program getting the simulation and experimental results .Figure (3.6) and figure (3.7) shows the results for different inputs. (A) Simulation result (B) Experimental result Figure (3.6) Simulation and experimental results for 1.02 volt (A) Simulation result (B) Experimental result Figure (3.7) Simulation and experimental results for 3.08 volt The 8 bits presented from ADC will be input the FPGA card where the intelligent controller implemented there. 3.2.3 Back-Propagation Algorithm (BP) For the training of Multi-Layer Perceptron (MLP), Rumelhart, Hinton and Williams in 1986 proposed the back propagation (BP) algorithm which is most popular supervised learning technique in ANN [38]. Basically, Bp learning consists of two passes through the different layers of the network as shown in Figure (3.8), a forward pass and backward pass. During the forward pass the
Open Document