Research On Self-Tuning Control Of The Parameters For The Time-Delay System

Time-delay systems are very widely used in industry and are hard to control, especially when the plant with a long delay. However, a lot of temperature control systems belong to time-delay systems. Although the classical intelligent temperature controller in the practical application of temperature control shows ideal control effect, it still adopts offline tuning methods to solve parameter setting and to control system separately. If the situation changes the parameter must be readjusted. Aimed at the problem, by combining of neural network control, fuzzy control, and PID control, a fuzzy adaptive PID controller based on neural network is designed to adjust parameters online to achieve high performance.Firstly, this thesis analyzes the characteristics of time-delay system, discusses some general control schemes for time-delay, for example, differential forward control algorithm, Smith predictor control algorithm, Dahlin control algorithm. And it analyzes their control performance. And the result of simulation shows the difference of their performance in temperature control systems.Secondly, the thesis describes the traditional principle of PID parameter control, and designes a improved scheme and its relevant controller. This controller comprehensively combines the advantage of the neural network control, fuzzy control and PID control. It possesses not only the simplicity control and strong logical inference of the fuzzy control, but also the learning and adaptive functions by using the neural network. Furthermore, it is as widely adaptive as PID controller. It realizes the PID parameters' tuning online and don't need offline tuning. It has proven that this controller has adaptability and robustness to the environment.Finally, the thesis adopts the fuzzy adaptive PID controller based on neural network to the B&R temperature control system. That could finish the PID parameters' tuning online and obtain perfect control effect. Theoretical analysis, simulation and experiment results have proven that the strategy could decrease the overshoot and shorten the tuning time, improve the system's real-time performance and precision.