PLANT-IDENTIFICATION WITH FUZZY INFERENCE AND ITS APPLICATION TO AUTOTUNING

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. At first, an input signal, such as a step signal, is given to both control loops. Then the parameters in the plant model are modified so that the output signal features, such as magnitude of an overshoot, of the two loops' become closer. Fuzzy rules describe the relationship between comparison results of the features and magnitudes of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested both in simulations for several plants with first-order lags and dead times, and in experiments for motor control. The results show that the proposed method is effective for practical use.