Adaptive optimal $\ell_\infty$-induced robust stabilization of minimum phase SISO plant under bounded disturbance and coprime factor perturbations

TL;DR

This paper develops an adaptive control method for minimum-phase SISO plants that guarantees optimal robust stabilization under bounded disturbances and uncertainties, using set-membership estimation and linear-fractional programming.

Contribution

It introduces a novel adaptive control approach that achieves optimal robust stabilization despite unknown parameters and non-identifiability, with online verification capabilities.

Findings

Guarantees prescribed accuracy in worst-case output bounds.

Reduces complex online optimization to linear-fractional programming.

Provides online validation of parameter estimates and assumptions.

Abstract

This paper addresses the problem of optimal robust stabilization of a discrete-time minimum-phase plant in the framework of robust control theory in the $\ell_1$ setup and under poor a priori information. Coefficients of the transfer function of the plant nominal model with stable zeros are unknown and belong to a known bounded polyhedron in the space of coefficients. The gains of coprime factor perturbations of the plant and the upper bound of external disturbance are also unknown. The problem under consideration is to design adaptive controller that minimizes, with the prescribed accuracy, the worst-case asymptotic upper bound of the output. Solution of the problem is based on set-membership estimation of unknown parameters and treating the control criterion as the identification criterion. A hard nonconvex problem of on-line computation of optimal estimates is reduced, under additional nonrestrictive assumption, to a linear-fractional programming via a nonlinear transformation of estimated parameters. Despite the non-identifiability of the unknown parameters, the proposed adaptive controller guarantees, with the prescribed accuracy, the same optimal asymptotic upper bound of the output of adaptive system as the optimal controller for the plant with known parameters. In addition to the optimality of adaptive control, the proposed solution provides on-line verification/validation of current estimates and a priori assumptions.