This paper is concerned with the mean-square exponential stabilization issue of memristive
neural networks (MNNs) subject to deception attacks via sampled-data control. The reasons …

Deep Learning (DL) systems are difficult to analyze and proving convergence of DL
algorithms like backpropagation is an extremely challenging task as it is a highly non …

A manipulator system may have both kinematics and dynamics uncertainties, which pose
difficulties in controller design. To solve the above problem, this study proposes an adaptive …

Iterative Learning Control (ILC) is known as a high-accuracy control strategy for repetitive
control missions of mechatronic systems. However, applying such learning controllers for …

In past decades, much progress has been obtained in vision-based robot control theories
with traditional image processing methods. With the advances in deep-learning-based …

As humans focus control only on task–space variables to achieve dexterous manipulation,
robots could strongly profit from advanced task-space control, which is still blocked by …

With the rapid development of deep learning, model-free reinforcement learning algorithms
have achieved remarkable results in many fields. However, their high sample complexity …

Robots are increasingly being employed for diverse applications where they must work and
coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any …

This paper presents two model-free control strategies for the rejection of unknown
disturbances in continuum robots. The strategies utilize a neural network-based …

This article studies finite-time adaptive fault-tolerant control for uncertain robotic manipulator
systems with guaranteed transient performance. Combining with backstepping method and …