Optimal Controller Identification for multivariable non-minimum phase systems.

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input-output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix - the reference model - is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods.

One-shot data-driven controller certification.

We present a one-shot, purely data-driven, method for controller certification. The method is based on an analysis presented in previous works, where theoretical results have been given showing that certification can be obtained by the estimation of the H-norm of a very specific transfer matrix, but without actually pursuing this approach. Accordingly, we propose a procedure for the estimation of the H-norm directly from closed-loop input-output data, which is based on the Markov parameters.

The generalized forced oscillation method for tuning PID controllers.

In this paper a generalized tuning methodology for proportional-integral-derivative (PID) controllers is proposed. The methodology is akin to the Ziegler-Nichols forced oscillation method, inheriting fully its practical appeal, but can be applied to much more general classes of plants. This generalization is achieved by employing a relay with adjustable phase (RAP) in a relay feedback experiment, and the tuning consists of formulas based on measurements obtained from this experiment.