Air conditioner temperature control system based on RBF neural network and control method thereof

Abstract

The invention relates to an air conditioner temperature control system based on an RBF neural system and a control method thereof. The system comprises a RBF neural network PID controller, a controlled object, a Smith predictive compensator and a feedforward controller, wherein the RBF neural network PID controller and the controlled object are connected between the system input end and the systemoutput end in sequence, and parameters of the PID controller are adjusted in a self-adaption mode by adapting the RBF neural network; the Smith predictive compensator is connected between the outputend and the input end of the RBF neural network PID controller; and the feedforward controller is connected between the output end of the RBF neural network PID controller and the system input end. According to the air conditioner temperature control system based on the RBF neural network and the control method thereof, the parameters of the PID controller are adjusted in the self-adaption mode based on the RBF neural network, meanwhile, the Smith predictive compensator and the feedforward controller are combined, the characteristics of high response speed, small overshoot, short adjusting time, good dynamic property, high self-adaptability and the like are achieved, meanwhile, online parameter setting is achieved, accordingly, the control performance of the system is greatly improved, anda satisfactory control effect is achieved.

Description

technical field [0001] The invention relates to the technical field of automatic control, in particular to an RBF neural network-based air-conditioning temperature control system and a control method thereof. Background technique [0002] Temperature control is the most intuitive factor to determine whether an air-conditioning system is effective. Intelligently controlling the temperature of an air-conditioned room can effectively improve the comfort of the room environment. [0003] At present, the temperature control of air-conditioned rooms has always been a hot and difficult point of research at home and abroad. The system in the air-conditioned room is a complex and changeable system. Its parameters such as temperature and humidity, personnel density, and equipment heat dissipation all have strong coupling. system. [0004] The traditional PID control has the advantages of simple structure and easy realization, and is widely used in industrial control. However, becau...

AI Technical Summary

Problems solved by technology

The control algorithm based on the combination of BP neural network and PID proposed by Jiang Dingguo has greatly improved the performance of the control system, but the disadvantages are that the learning rate and convergence rate of BP neural network are slow and the training time is too long

However, the control principles and structures of these methods are relatively complicated, which makes the tuning of control system parameters more difficult, which is not conducive to the popularization and application of control algorithms.

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