In recent years, numerous attempts have been made to integrate sliding mode control (SMC)
and neural networks (NN) in order to leverage the advantages of both methods while …

Artificial intelligence and mechanical engineering are two mature fields of science that
intersect more and more often. Computer-aided mechanical analysis tools, including …

In this article, a robot skills learning framework is developed, which considers both motion
modeling and execution. In order to enable the robot to learn skills from demonstrations, a …

This paper focuses on a new finite-time convergence disturbance rejection control scheme
design for a flexible Timoshenko manipulator subject to extraneous disturbances. To …

Considering the bias of the dynamics which is a global trend of the dynamical equation of a
robot manipulator because of the gravity or the constant payloads, two kinds of adaptive bias …

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

Parameter uncertainties and fluctuated disturbances have brought great difficulties to the
smooth and precise control of robot manipulators in some industrial environments. To …

This study focuses on an adaptive fault-tolerant boundary control (BC) for a flexible string
(FS) in the presence of unknown external disturbances, dead zone, and actuator fault. To …

In this paper, the Intelligent Impedance Control based Wavelet Neural Network (IIC-WNN) is
introduced as a noble adaptive variable impedance approach to enhance the efficiency of …

In the present study, the adaptive sliding mode control (ASMC) strategy is investigated for a
class of complex nonlinear systems with matched and unknown nonlinearities and external …