System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

TL;DR

This paper introduces two new convex parameterizations of stabilizing controllers, compares their properties with existing ones, and analyzes their numerical robustness and approximation capabilities under FIR constraints.

Contribution

The paper reveals four equivalent groups of stable closed-loop transfer matrices, introduces two new convex parameterizations, and studies their robustness and approximation properties without requiring doubly-coprime factorizations.

Findings

The IOP best approximates stabilizing controllers under FIR constraints.

The IOP is numerically robust for open-loop stable plants.

The four parameterizations require equality constraints that are not exactly satisfiable in practice.

Abstract

It is known that the set of internally stabilizing controller $\mathcal{C}_{\text{stab}}$ is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the Youla parameterization, and two recent notions are the system-level parameterization (SLP) and the input-output parameterization (IOP). In this paper, we address the existence of new convex parameterizations and discuss potential tradeoffs of each parametrization in different scenarios. Our main contributions are: 1) We reveal that only four groups of stable closed-loop transfer matrices are equivalent to internal stability: one of them is used in the SLP, another one is used in the IOP, and the other two are new, leading to two new convex parameterizations of $\mathcal{C}_{\text{stab}}$. 2) We investigate the properties of these parameterizations after imposing the finite impulse response (FIR) approximation, revealing that the IOP has the best ability of approximating $\mathcal{C}_{\text{stab}}$ given FIR constraints. 3) These four parameterizations require no \emph{a priori} doubly-coprime factorization of the plant, but impose a set of equality constraints. However, these equality constraints will never be satisfied exactly in floating-point arithmetic computation and/or implementation. We prove that the IOP is numerically robust for open-loop stable plants, in the sense that small mismatches in the equality constraints do not compromise the closed-loop stability; but a direct IOP implementation will fail to stabilize open-loop unstable systems in practice. The SLP is known to enjoy numerical robustness in the state feedback case; here, we show that numerical robustness of the four-block SLP controller requires case-by-case analysis even the plant is open-loop stable.