Development Of A CAN Bus Based Field Level Monitoring System For Greenhouse Environment

With the fast development of the electronic technology, the greenhouseenvironment control systems become more and more intellectualized. Therealization of greenhouse modernization and industrialization necessitatesthe perfection of system structures and improvement to the existing controlmethods by employing computer technology, information and networktechnology, and intelligent control technology. In this dissertation, a CANbus based field level monitor and control system was developed for thegreenhouse environment.The primary factors affecting the greenhouse environment wereanalyzed and the corresponding control systems and implementingmechanisms were studied. Then three kinds intelligent CAN bus nodes,including analog signal collection, digital signal collection and on-offcontrol ones, were developed using the modular design method. In all, thefield level greenhouse monitor and control system comprises an IPC, CANinterface cards, intelligent CAN bus nodes and some other sensors. The system adopted the CAN bus method to realize the communicationbetween the IPC and the intelligent CAN Bus nodes. The communicationprotocol, together with the communication program module including CANmodule initialization, CAN data reception and transmission, was designedbased on the frame structure using CAN2.0A standard. In the system, TheIPC takes the initiative role in controlling the greenhouse environment bysending commands to the intelligent CAN nodes for data collection andother operations.The fuzzy control method was used in the system as the controlstrategy according to the characteristics of greenhouse environment. Basedon the analysis of the inputs, outputs and the control rules of thegreenhouse environment, a fuzzy controller was designed for the controlsystem. After that, a dynamic greenhouse environment model wasconstructed and simulations were carried out using Matlab/Simulink tools.The results got a good agreement with the experiment data, which provedthe accuracy of the model and the validity of the control strategy. In the end,the outcome of fuzzy control was compared with that of PID control. It wasconfirmed that the fuzzy control with a smaller overshoot and adjustingtime constant is superior to the PID control.