Design of an Implicit Self-tuning PID Controller Based on the Generalized Output

Abstract

PID controllers have been widely employed in real processes. Since PID parameters strongly affect the control performance, lots of schemes for tuning PID parameters have been proposed. Fixed PID controllers are applied mainly. However, it is impossible to obtain good control performance for time-variant systems by the fixed PID controllers. Therefore, it is important to tune PID parameters in an on-line manner. In addition, especially in process systems, it is difficult to disturb the systems to identify. Since, it is important to determine PID parameters directly from the closed-loop operating data. In this paper, in order to overcome these problems, an implicit self-tuning PID control scheme is proposed. In the proposed method, a new parameter tuning law using obtained closed-loop data is introduced, and recursive least squares method is applied to the scheme. This scheme has a reference model which can be designed by users. As a result, the system output can track the desired reference model output. The new parameter tuning law can calculate PID gains directly from closed-loop data, so the proposed method belongs to implicit schemes. Therefore, a system identification can be avoided. In addition, procedures of the scheme are very simple. Hence, the computation cost becomes low. The effectiveness of the control scheme is evaluated by an experimental example.