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

In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform
the trajectory control of redundant robot manipulators using remote center of motion (RCM) …

In this work, a sliding mode control (SMC) method and a composite learning SMC (CLSMC)
method are proposed to solve the synchronization problem of chaotic fractional-order neural …

The issue of finite-time stabilization (FTS) for the memristor-based inertial neural networks
(MINNs) with mixed time-varying delays (MTVDs) is researched by virtue of a new analytical …

Parameter convergence is desirable in adaptive control as it enhances the overall stability
and robustness properties of the closed-loop system. However, a stringent condition termed …

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 …

This article considers neural network (NN)-based adaptive finite-time resilient control
problem for a class of nonlinear time-delay systems with unknown fault data injection attacks …

This paper investigates the state recovery and disturbance estimation of unmanned surface
vehicles in the presence of unknown disturbances as well as unmeasured surge, sway, and …

This article presents an adaptive control method for dual-arm robot systems to perform
bimanual tasks under modeling uncertainties. Different from the traditional symmetric …

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