Parameters Of Arm-based Self-tuning Pid Regulator

The PID control is the most common control algorithm. PID control is widely applied to chemical, metallurgical, mechanical, thermoelectrical and light industrial process control system because of its relatively simple structure, which can be easily understood and implemented in practice. So that many sophisticated control strategies are based on it. The tuning of PID controllers, usually done by seasoned engineers and technicians, is laborious and time-consuming. Furthermore, because the real processes are different in dynamics, and have time delay or nonlinearity, the tuning of PID sometimes becomes very difficult. As a result, many regulators are in practice poorly tuned, so it is important to study auto-tuning PID control.Chapter 1 is an introduction of the dissertation. The background and significance are firstly described, then currently domestic and international research situation are summed up. According to PID self-tuning's problems in practice at present, the dissertation puts forward its main targets.Chapter 2 studies the classic Ziegler-Nichols enginerring tuning method. But the characteristic parameters which obtained by identification of relay feedback has great error in case of big loading disturbance because of the asymmetry output and the asymmetry period caused by loading disturbance. Aming at this problem, the hysteresis relay feedback is used which can counteract the asymmetry. Thereby, the error is minished and the result of identification is more precise. A new identification method based on hystersis relay feedback is putted forward which can obtain second order plus dead time(SOPDT) model of the process by frequency response at multipoints, then two self-tuning methods of PID control are designed based on the SOPDT, which are direct synthesis method and stable abundant method. Simulation results demonstrate that both methos can make the controlled plan have good control performance in terms of disturbance rejecton and controlling. They are simple and efficient, and are the same with real-time industry process control online.Chapter 3 studies the self-tuning PID control based on neural network.