This paper puts forward a novel deep reinforcement learning control using deep
deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking …

Most researches about control of flexible manipulators are all based on the dynamic model,
which is difficult to establish because of their flexibility and the tedious process of measuring …

This article discusses the control design and experiment validation of a flexible two-link
manipulator (FTLM) system represented by ordinary differential equations (ODEs). A …

As a research hotspot in the field of artificial intelligence, the application of deep
reinforcement learning to the learning of the motion ability of a manipulator can help to …

This article proposes real-time speed tracking of two-link surface swimming robotic fish
using a deep reinforcement learning (DRL) controller. Hydrodynamic modelling of robotic …

Although the soft robot is gaining considerable popularity in dexterous and safe
manipulation, accurate motion control is still an open problem to be explored. Recent …

The Stewart platform is an entirely parallel robot with mechanical differences from typical
serial robotic manipulators, which has a wide application area ranging from flight and driving …

Specific satellites with ultra-long wings play a crucial role in many fields. However, external
disturbance and self-rotation could result in undesired vibrations of flexible wings, which …

In this study, we apply deep reinforcement learning (DRL) to control a robot manipulator and
investigate its effectiveness by comparing the performance of several DRL algorithms …

Reinforcement Learning (RL) is gaining much research attention because it allows the
system to learn from interacting with the environment. Yet, with all these successful …