Non-iterative data-driven controller tuning using the correlation approach

Data-driven controller tuning for the model-reference control problem is investigated. A new controller-tuning scheme for linear time-invariant single-input single-output systems is proposed. The method, which is based on the correlation approach, uses a single set of input/output data taken in open-loop or closed-loop operation. A specific choice of instrumental variables makes the correlation criterion an approximation of the model-reference control criterion. The correlation criterion and the controller parameters are asymptotically not affected by noise. Although for finite data length the criterion is biased, a bias analysis shows that it generally improves the controller robustness. The effectiveness of the proposed method is illustrated via a simulation example.