Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

Composite adaptation and learning techniques were initially proposed for improving
parameter convergence in adaptive control and have generated considerable research …

Executing safe and precise flight maneuvers in dynamic high-speed winds is important for
the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the …

This article proposes a gradient descent (GD) algorithm-based B-spline wavelet neural
network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under …

This article considers the robust optimal control problem for a class of nonlinear systems in
the presence of unmodeled dynamics. An adaptive optimal controller is designed using the …

Three types of high-order system models with parametric uncertainties are introduced,
namely, the high-order fully actuated (HOFA) models, and the second-and high-order strict …

A universal, approximation-free state feedback control scheme is designed for unknown
pure feedback systems, capable of guaranteeing, for any initial system condition, output …

In this paper, we present control designs for single-input single-output (SISO) nonlinear
systems in strict feedback form with an output constraint. To prevent constraint violation, we …

In this brief, adaptive neural control is presented for a class of output feedback nonlinear
systems in the presence of unknown functions. The unknown functions are handled via on …

Implementation of adaptive backstepping controllers requires analytic calculation of the
partial derivatives of certain stabilizing functions. It is well documented that, as the order of a …