3 research outputs found
Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties
This paper presents a robust adaptive control solution for linear
parameter-varying (LPV) systems with unknown input gain and unmatched nonlinear
(state- and time-dependent) uncertainties based on the adaptive
control architecture and peak-to-peak gain (PPG) analysis/minimization from
robust control. Specifically, we introduce new tools for stability and
performance analysis leveraging the PPG bound of an LPV system that is
computable using linear matrix inequality (LMI) techniques. A
piecewise-constant estimation law is introduced to estimate the lumped
uncertainty with quantifiable error bounds, which can be systematically
improved by reducing the estimation sampling time. We also present a new
approach to attenuate the unmatched uncertainty based on the PPG minimization
that is applicable to a broad class of systems with linear nominal dynamics. In
addition, we derive transient and steady-state performance bounds in terms of
the input and output signals of the actual closed-loop system as compared to
the same signals of a virtual reference system that represents the possibly
best achievable performance. Under mild assumptions, we prove that the
transient performance bounds can be uniformly reduced by decreasing the
estimation sampling time, which is subject only to hardware limitations. The
theoretical development is validated by extensive simulations on the
short-period dynamics of an F-16 aircraft