On a Convex Parameter Space Method for Linear Control Design of Uncertain Systems

José C. Geromel, Pedro L. D. Peres, J. Bernussou

SIAM Journal on Control and Optimization · 1991 · 357 citations · 18 references

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TL;DR

The paper introduces a new procedure for designing continuous and discrete‑time linear control systems. The method formulates a convex programming problem in parameter space that yields a feedback gain and applies to decentralized control and quadratic stabilizability of uncertain systems with dynamic and input uncertainties, illustrated by examples. The procedure enables easy incorporation of design constraints and provides necessary and sufficient conditions for a linear stabilizing gain, making it one of the first numerical methods to solve high‑order uncertain system stabilization problems.

Abstract

This paper presents a new procedure for continuous and discrete-time linear control systems design. It consists of the definition of a convex programming problem in the parameter space that, when solved, provides the feedback gain. One of the most important features of the procedure is that additional design constraints are easily incorporated in the original formulation, yielding solutions to problems that have raised a great deal of interest within the last few years. This is precisely the case of the decentralized control problem and the quadratic stabilizability problem of uncertain systems with both dynamic and input uncertain matrices. In this last case, necessary and sufficient conditions for the existence of a linear stabilizing gain are provided and, to the authors’ knowledge, this is one of the first numerical procedures able to handle and solve this interesting design problem for high-order, continuous-time or discrete-time linear models. The theory is illustrated by examples.

References

18