We apply the Iterative Feedback Tuning (IFT) method to the tuning of PID parameters in applications where the objective is to achieve a fast response to set point changes. We compare the performance of these IFT-tuned PID controllers with the performance achieved by four classical PID tuning schemes that are widely used in industry. Our simulations show that IFT always achieves a performance that is at least as good as that of the classical PID tuning schemes, and often dramatically better: faster settling time and less overshoot. In addition, IFT is also optimal with respect to the presence of noise, whereas the other schemes are designed for noise-free conditions. The IFT method used here is a variant of the initial IFT scheme, in which no weighting is applied to the control error during a time window that corresponds to the transient response, and where the lenght of this window is progressively reduced. This method was initially proposed in (Lequin, 1997), and elaborated on in (Lequin et al., 1999).

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The problem of estimating the parameters in continuous-time autoregressive moving average (ARMA) processes from discrete-time data is considered. Both direct and indirect methods are studied, and similarities and differences are discussed. A general discussion of the inherent difficulties of the problem is given together with a comprehensive study on how the choice of the sampling interval influences the estimation result. A special focus is given to how the Cramér-Rao lower bound depends on the sampling interval.

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