A parameter self-learning proportion- cycle algorithm in the application of temperature control system

When the environment temperature changes violently, the precise instrument equipments to request to the temperature accuracy and the system stability also raises immediately. In this paper, we propose a parameter self-learning proportion-cycle algorithm for temperature control. Our algorithm is able to identify system parameters and control parameters automatically, and have self- learning ability. When the environment temperature changes violently, come up with proper control strategies with respect to environment temperature, as a result, it can be applicable to objects whose system parameters are unknown. The simulation results show that this algorithm achieves excellent performances for temperature control with high precision and self-adaptation, thus can meet strict needs of the precise instrument equipments.