A PID controller with neuron tuning parameters for multi-model plants

Because there exist uncertainties in practice, a real process often presents multi-model dynamic characteristics. Therefore, it is not easy to reach satisfaction performance by using a conventional PID controller. In this paper, the PID control method with neuron tuning parameters is proposed for multi-model plants. In this model-free control system, the PID controller is designed to control a multi-model plant and the adaptive neuron is used to regulate the parameters of the PID controller on line. By self-learning and associative searching, the adaptive neuron can modify the PID controller parameters according to the dynamic characteristics of the plant. Applying the proposed method to the basis weight control of a paper machine, the simulation experiments are made. The results illustrate that the model-free PID controller is available to multi-model plants.