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

A key assumption in the development of system identification and adaptive control schemes
is the availability of a regression model which is linear in the unknown parameters (of the …

In this article, we propose a way to enhance the learning framework for zero-sum games
with dynamics evolving in continuous time. In contrast to the conventional centralized actor …

This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …

Wind farms' power-generation efficiency is constrained by the high system complexity. A
novel deep reinforcement learning (RL)-based wind farm control scheme is proposed to …

Adaptive dynamic surface control (ADSC) is effective for solving the complexity problem in
adaptive backstep** control of integer-order nonlinear systems. This article focuses on the …

Concurrent learning (CL) is a recently developed adaptive update scheme that can be used
to guarantee parameter convergence without requiring persistent excitation. However, this …

Friction is one of the significant obstacles that hinders high-performance robot tracking
control because accurate friction modeling and effective compensation are challenging …

Most recent studies on adaptive hydraulic tracking control focus on the trajectory tracking
performance while the parameter convergence property is often unsatisfying. This article …

Most current studies of adaptive robot control concentrate on parameter convergence in the
steady state, while parameter convergence rates are rarely investigated. This paper …