Effective Control Method Based on Trajectory Optimization for Three-Link Vertical Underactuated Manipulators With Only One Active Joint

Wang, Lejun; Lai, Xuzhi; Meng, Qingxin

doi:10.1109/tcyb.2021.3125187

Cited by 10 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Free-flying manipulators are characterized by the active control of position and orientation (attitude) of the base spacecraft through the use of thrusters, while the manipulator executes its designated tasks (Seddaoui et al, 2021). This approach, which is analogue to the operation of a fixed-base manipulator, has been extensively studied with focus on path planning and control (Pfeiffer, 1968;Wang et al, 2021). However, the reliance on the thrusters for pose (position and attitude) control incurs significant fuel consumption, potentially reducing the operational lifespan of the system in orbit.…”

Section: Open Access Edited Bymentioning

confidence: 99%

Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise

Al Ali,

Zhu

2024

Front. Control Eng.

View full text Add to dashboard Cite

This study introduces a novel approach for the path planning of a 6-degree-of-freedom free-floating space robotic manipulator, focusing on collision and obstacle avoidance through reinforcement learning. It addresses the challenges of dynamic coupling between the spacecraft and the robotic manipulator, which significantly affects control and precision in the space environment. An innovative reward function is introduced in the reinforcement learning framework to ensure accurate alignment of the manipulator’s end effector with its target, despite disturbances from the spacecraft and the need for obstacle and collision avoidance. A key feature of this study is the use of quaternions for orientation representation to avoid the singularities associated with conventional Euler angles and enhance the training process’ efficiency. Furthermore, the reward function incorporates joint velocity constraints to refine the path planning for the manipulator joints, enabling efficient obstacle and collision avoidance. Another key feature of this study is the inclusion of observation noise in the training process to enhance the robustness of the agent. Results demonstrate that the proposed reward function enables effective exploration of the action space, leading to high precision in achieving the desired objectives. The study provides a solid theoretical foundation for the application of reinforcement learning in complex free-floating space robotic operations and offers insights for future space missions.

show abstract

Section: Open Access Edited Bymentioning

confidence: 99%

Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise

Al Ali,

Zhu

2024

Front. Control Eng.

View full text Add to dashboard Cite

show abstract

“…For underactuated mechanical systems, the number of their control inputs is less than their degrees of freedom. Such systems are not completely controllable [2][3][4][5]. To promote social progress, various underactuated mechanical systems are widely applied, such as underactuated manipulators [6][7][8], cranes [9][10][11], helicopters [12][13][14], quadrotors [15][16][17], unmanned ships [18,19], and soft robots [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Gain-Scheduled Model Predictive Control for Cart–Inverted-Pendulum with Friction and Disturbances

He,

Li,

Wei

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.

show abstract

Trolley regulation and swing reduction of underactuated double-pendulum overhead cranes using fuzzy adaptive nonlinear control

Miao

Zhao

Wang³

et al. 2022

Nonlinear Dyn

View full text Add to dashboard Cite

Effective Control Method Based on Trajectory Optimization for Three-Link Vertical Underactuated Manipulators With Only One Active Joint

Cited by 10 publications

References 46 publications

Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise

Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise

Gain-Scheduled Model Predictive Control for Cart–Inverted-Pendulum with Friction and Disturbances

Trolley regulation and swing reduction of underactuated double-pendulum overhead cranes using fuzzy adaptive nonlinear control

Contact Info

Product

Resources

About