Neural Network Pid In The Heat Exchanger

As the requirements on the quality of life improved, the room temperature change of the indicators put forward the requirements.It is a very common phenomenon, the complexity, time-varying and time-delay characteristics system has widely used in daily life in the heating temperature control. It is difficult to establish due to complex non-linear mathematical model of precision, so that disturbances and the regulation can not be reflected in timely in the process of energy transfer in time-delay ,thus greatly reducing the stability control system performance, easily lead to larger overshoot and longer settling time and serious impact on the quality control system.Although PID control has been playing an enormous role, but for the ordinary PID control it is very difficult to be satisfied with the control accuracy and effectiveness like system of complex non-linear and difficult to establish accurately model.This paper introduces the heating system and its modeling, then chiefly introduced two forms of PID control - the location style and incremental style , and the imitation test is done.The fourth chapter is chiefly introduced the NNPID in temperature control, BP neural network PID controller and the parameters self-tuning , its learning algorithm is derived and imitation test, including step response curve and sine tracking wave curve. imitation result shows that the adaptive system has the capacity to change and time-delay system.Chapter V introduces DRNN apply to temperature control , DRNN used to identify the sensitivity information on the control input changes to output of object.The imitation result shows that the DRNN-based control of dynamic interference BPPID have very good control, and improve the control precision . A lot of imitations have been made, the results indicate that controller using the control algorithm and the controller of nonlinear and time-delay has better adaptability and robustness.