Direct tuning of PID controller and reference model with input constraint

The direct tuning of controller parameters based on data-driven control has attracted considerable attention because of its simple controller design. In this study, we proposed a direct tuning method based on a fictitious reference signal to obtain controller and reference model parameters. In the method, predicted input/output data with respect to controller parameters are used. The predicted data with pole information are obtained based on instability detecting-fictitious reference iterative tuning, which can ensure bounded-input, bounded-output stability. We derived a new objective function with constraints using the predicted data to automatically obtain controller and reference model parameters to perform a fast response under model matching and input constraints. The function consists of an evaluation part in which the fastest responsive reference model is requested and constraint parts that include model-matching errors and input amounts. A simulation was performed to verify the effectiveness of the proposed method. The results showed that the proposed method provided controller and reference model parameters for the model matching and input constraints from one-shot data without trial and error.