| Controlling the environment of the greenhouse is a multi-input, multi-output, and nonlinear control problem. It is difficult to obtain ideal results sometimes to control with conventional control methods. Therefore, it is necessary to introduce intelligent control method to solve those complex problems that can’t solved by traditional methods to develop a control system which can meet China’s national conditions and to improve the control precision of the greenhouse environment.Having read some relative literature and having analyzed the physical processes of heat and mass transfer by radiation, ventilation, convection and crop transpiration, this research establishes a model of the greenhouse temperature and humidity which is based on the law energy conservation and material conservation. It collects data in the greenhouse of Nanjing Agricultural University, simplifies the model, and tests the model in the computer simulation. The results show that the model can well predict the greenhouse air temperature and humidity.This research use the second-order system H(s)=20/1.6x2+4.4s+1as a controlled object to inspect the control performance of PID control, optimal control, robust control, expert PID control, fuzzy adaptive tuning PID control and RBF network monitoring PID control. The method of membership function of fuzzy mathematics is used to conduct a comprehensive evaluation of the6control systems in5indicators (rise time tr, settling time ts, peak time tp, overshoot8p, steady state error|ess|). The evaluation shows that fuzzy adaptive tuning PID control has the best control performance. This research analyzes the advantages and disadvantages of each control algorithm and determines to use fuzzy control as the main control algorithm to solve the control problem.This dissertation introduces a fuzzy control system designed to control the temperature of the greenhouse in the heating season. It details the design and implementation of fuzzy input variables, fuzzy output, control rules, control strategy, and anti-fuzzy output. MATLAB/Simulink is used to build the system model and set the delay module. Model uses the Runge-Kutta algorithm of the fixed step to do the integration, step size is1. In the condition that temperature of outdoor is10℃, solar radiation is200W·m-2, setting temperature is15℃, greenhouse control is simulated. Although the result has a little shock, it still shows a good control performance.PID control is the one of the early developed control strategies. It is used widely in various control systems. Having analyzed the combination of PID and fuzzy control in various forms, this research decides to use PID to adjust the quantization factor and scale factor of the fuzzy control on line. Having integrated PID control, the control performance of the control system is improved.If the fuzzy control rules are drafted by man, the design of the control system will depend on the expertise and it may not be the optimal one. This research compares the particle swarm optimization and the genetic algorithm optimization, and the simulation result proves that PSO is more suitable for the optimization requirements of this research. Having used the particle swarm optimization algorithm to optimize the fuzzy rules, the control performance of the system is much better.Although using PID to adjust the factors of the fuzzy control on line can increase the scope of application of the fuzzy control, it also has restraint scope to use fuzzy control. According to bagging algorithm, this research combines5optimized PID fuzzy controllers to solve the control problem. Simulation results show that this method of control is better than automatic control, the original system control and the PID controllers which compose of the final system, and it has a wider application scope. |
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