Design of sampled-data systems with large plant uncertainty using quantitative feedback theory

Abstract

This paper proposes a new quantitative feedback theory (QFT) design framework for dealing with sampled-data systems with large plant uncertainty. After the QFT-based design in the continuous-time domain is completed, the analogue controller can be transformed directly into a rational discrete-time transfer function via approximate Z transform, with the sampling time as a free parameter. The sampling time can therefore be adjusted to make the uncertain sampled-data system robustly stable. In comparison with other approaches, our approach is much more systematic without the solvability problem and yet significant enough to guide the designer to realize the physical controller in which the plant transfer function has prescribed bounds on its parameters. Several examples are used to illustrate the proposed approach and excellent results are obtained.