Research On Intelligent Temperature Control Algorithms For Aircraft Ground Atr-conditioning Cars

Aircraft ground air-conditioning cars work in a complicated external environment,which cause the temperature control system of air-conditioning cars to have the characteristicsof large time delays, nonlinearity, time-varying and working status uncertainty. Thetemperature control systems are mostly based on the conventional PID control algorithm; andthe algorithm, for imprecise control models, has the defects of high overshoot, slow responseand weak anti-interference ability and so on. Therefore, the simple usage of conventional PIDalgorithm is difficult to meet the temperature control requirements of the air-conditioning carapplications. With the rapid development of intelligent control theory and technology, thecombination of intelligent algorithms and control theories is the main way to solve theproblem of complex temperature system control.The neural network and the fuzzy control are introduced into the conventional PIDcontrol algorithm; by combining these algorithms with PID, simulation comparison arecompleted in this paper. The experimental results show that, the effect of fuzzy PID control isbetter in stability and precision, but poor in adaptive adjustment; neural network based PIDcontrol has the ability of anti-jamming and speed adjustment, but existing knowledge andexperience cannot be used, which causes the algorithm to have the defect of high overshoot.Based on the study of neural networks and fuzzy control algorithm, a new fast response andstrong anti-jamming variable fuzzy CMAC-PID control method is designed in this paper. Themethod takes advantage of a stronger adaptive ability of the cerebella network to work inparallel with the fuzzy PID controller, and is able to reach the required temperature rapidly,accurately and stably. The simulation results by Matlab show that, the control method caneffectively improve the system dynamic performance, steady-state accuracy and robustness.