Most current model reference adaptive control (MRAC) methods rely on parametric adaptive
elements, in which the number of parameters of the adaptive element are fixed a priori, often …

Roll angular motion of the modern aircraft operating in non-linear flight modes with a high
angle of attack often demonstrates the limit cycle oscillations, which is commonly known as …

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the
system can be expressed in the linear span of a set of known basis functions. While this …

Gaussian Process State Space Models (GP-SSMs) are a non-parametric model class
suitable to represent nonlinear dynamics. They become increasingly popular in data-driven …

Nonparametric modeling approaches show very promising results in the area of system
identification and control. A naturally provided model confidence is highly relevant for …

This two-part work presents a novel theory for model reference adaptive control (MRAC) of
deterministic nonlinear ordinary differential equations (ODEs) that contain functional …

Real-world dynamical variations make adaptive control of time-varying systems highly
relevant. However, most adaptive control literature focuses on analyzing systems where the …

For a nonlinear system with unmodeled dynamics and output disturbances, an adaptive
neural network based control strategy is designed. For the unknown output interference, this …

The performance of adaptive estimators that employ embedding in reproducing kernel
Hilbert spaces (RKHS) depends on the choice of the location of basis kernel centers …

Many current model reference adaptive control methods employ parametric adaptive
elements in which the number of parameters are fixed a-priori and the hyperparameters …