| This study comes from the cooperation projects: moisture analyzer between Shanghai Liang Ping Instruments Co. Ltd and our laboratory. Drying Oven is an important part of moisture analyzer, and the temperature control is one of the key techniques of moisture analyzer including heat transfer, dynamic temperature measurement and process control etc. According to market research and project demand analysis, the heating methods of drying oven of moisture analyzer are mainly divided into two categories, one is for the sample of the high sugar content, easy to coking dry filtration process, the temperature should rise softly; Another class is in view of the need of rapid water loss measurement process.Seeing that accuracy temperature measurement is the precondition of temperature control, much attention was paid to the static errors and dynamic errors of Pt100 temperature sensor in order to reduce the big overshoot of temperature caused by the lag of temperature sensor in this thesis. The mathematical model of Pt100 temperature sensor was built to meet the needs of dynamic compensation. Based on the model, the real-time time constant was estimated by using two Pt100 temperature sensors with different sizes, and then compensated the dynamic error of Pt100 temperature sensor. An adaptive method was adopted to reduce the static error of temperature control. The experiment results showed that the compensated Pt100 becomes faster. At the same time, some feasible measures base on hardware was put forward in order to reduce the error between the samples and the temperature sensor, which could guarantee the accurate temperature control in the oven effectively.The infrared drying oven is nonlinear, time-varying, and asymmetrical, the regular PID controller has constant parameters, so it can't obtain high accurate. This thesis studies the PID controller, analyzes the effects of proportional, integral and differential, and uses the trial-and-error method tuning the PID parameters on the drying oven, Based on the rules, a fuzzy-PID controller is designed. The scale and initial value of the parameters are given by means of trial-and-error, in the heating process, the fuzzy controller takes the error of temperature and its first order differential as inputs, and after the fuzzification of inputs, fuzzy reasoning and de-fuzzification of outputs, the real time PID parameters are obtained. In this way, the self-adjusting of PID parameters is realized, and it improves the accurate of temperature control.In order to improve the response speed furthermore, this thesis also use a complex intelligent control method, in the heating process, the controller takes flexible decisions according to the features of system and the humanoid mapping rules, including Bang-Bang control, proportional control and PID control, so this method has both fast response and high accurate. In order to increase the adaptive of the method, the parameter of the proportional control is tuning on-line by a fuzzy logic reasoning machine. After simplified, the algorithm runs in a drying oven temperature control system based on single chip microcomputer.Practical application shows that the designed fuzzy PID control method have the characteristics of small overshoot and high steady precision;The human-simulated intelligent control method based on rules has a certain overshoot, but rising rapidly. The two methods can satisfy different samples of moisture analyzer drying requirements. |
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