Research On Adaptive Control In Test Room Air-Conditioning Systems

At present our country has paid more and more attention to the energy problems. The proportion of the energy consumption in buildings to energy consumption in total country has exceeded 25%, and the proportion of the energy consumption of HVAC (heating, ventilation and air-conditioning) to the total energy consumption in a building has been close to 50%. Improving the operation and control level in a HVAC system will reduce energy consumption in a building apparently, and then release the problem of energy absence. For the characteristics of delay time, time-variation and nonlinearity in an air-conditioning system,it makes traditional PI/PID control method can't get good control performance, and then the phenomena of over-heating and over-cooling often appear and more energy will be consumed. Some adaptive control strategies and relevant system identification methods fit for air-conditioning systems were put forward in this paper. The research object is the air-conditioning system in a test room of the testing platform of air enthalpy-difference method. The specialties of the air-conditioning system in the test room can represent the majority of air-conditioning systems. Above adaptive control strategies and relevant system identification methods were applied to the air-conditioning system in the test room. Also a remote control system of refrigeration & air-conditioning testing-platform based on Internet which adopts adaptive Fuzzy PI control algorithm was developed in this paper. The primary research and achievement are: At first the mathematical model of the air-conditioning system in a test room of the testing platform of air enthalpy-difference method was built by mechanism. Then the simulation environment of the air-conditioning system was set up by MATLAB/Simulink. The theoretical model was validated by experiment using step response.An air-conditioning system must be identified when applies self-tuning control to the air-conditioning system. For a common air-conditioning object can be considered as first-order-plus-delay-time model and the algorithm of recursive least squares can't identify the delay time, the paper put forward two identification methods: RLS with exponential forgetting combined with model of zero frequency method and gradient method. The two identification methods can estimate the parameters of the air-conditioning object including delay time, and they have the advantages of simple, fast and fit for real-time control. The identification methods were applied to the air-conditioning system in the test room by simulation research. A self-tuning PI control strategy with robustness was put forward in the paper; the self-tuning control strategy adopts the RLS with exponential forgetting combined with model of zero frequency method which is used to estimate the parameters of an air-conditioning model under close-loop. In addition for the time constant of an air-conditioning model is always large, the paper designed a PI tuning formula with robustness which can guarantee the real air-conditioning process with gain-margin Am=2.98 and phase-marginφm=59.8°.The self-tuning PI control strategy with robustness, a self-tuning PI control strategy based on H∞and a self-tuning PI control strategy based on the Z-N tuning formulas were applied to the air-conditioning system in the test room and their control performances were compared among each other. For the delay time of an air-conditioning model will reduce the control performance, self-tuning control strategy with Smith predictor was put forward in this paper. The self-tuning control method also uses above combination algorithm to estimate the parameters of an air-conditioning model including delay time, and a Smith predictor is adopted to realize accurate compensator for the an air-conditioning model's delay time. The control performance of the self-tuning control with predictor was compared to a well tuned PID controller; the simulation results show the self-tuning control algorithm has better control performance...