Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

Hu, Hongjun; Xiao, Shungen; Shen, Haikuo

doi:10.1155/2021/8892032

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…erefore, trajectory planning in joint space is generally used for trajectory planning that does not require high motion trajectories or has a short motion distance and can ignore the influence of motion trajectories, such as point-to-point motion. In the joint space, three polynomial interpolation methods commonly used in path planning methods are high-order polynomial interpolation, parabolic interpolation, and spline curve interpolation [10,11].…”

Section: Trajectory Planning Of Spraying Robotmentioning

confidence: 99%

Motion Trajectory Planning and Design of Material Spraying Service Robot

Wang

Wen

Feng

et al. 2022

Advances in Materials Science and Engineering

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The application of robots reduces repetitive and dangerous tasks for humans, especially the spraying robot, because many spraying materials are corrosive, toxic, and harmful. This paper designs the motion trajectory planning of the material spraying service robot. With the increasing demand for technology, there are strict requirements for the uniformity and thickness of spraying. In view of this, this paper proposes an algorithm for modeling kinematics using a joint screw and optimizes the modeling algorithm using particle swarm optimization. This makes the industrial spraying robot more intelligent and more capable of completing high-standard tasks. The experimental results in this paper show that when the spraying radii on the large-curvature cone surface are 44.5 and 49.5 mm, respectively, the coating distribution in the intersection area of the curved surfaces can be well controlled, and the optimized algorithm can better plan the path of the spraying robot.

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Section: Trajectory Planning Of Spraying Robotmentioning

confidence: 99%

Motion Trajectory Planning and Design of Material Spraying Service Robot

Wang

Wen

Feng

et al. 2022

Advances in Materials Science and Engineering

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“…In the actual operation process, there may be errors in the joint operation of the robot, which will cause small fluctuations [24]. But this will not affect the tracking performance.…”

Section: Robot Displacement Trajectory Tracking Experimentsmentioning

confidence: 99%

“…Equating both ends(1,3) and (2,3) of formula(24), it can get−d 2 e 4 − d 4 e 3 = Fd 2 − He 2 , d 3 d 4 − e 3 e 4 = −Hd 2 − Fe 2 : ( ð25Þ It combines formula (21) and formula (25) to get…”

mentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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Active Disturbance Rejection Control of Flexible Joint System

Qiang,

Ahmad

2024

Lecture Notes in Electrical Engineering

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Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

Cited by 9 publications

References 26 publications

Motion Trajectory Planning and Design of Material Spraying Service Robot

Motion Trajectory Planning and Design of Material Spraying Service Robot

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Active Disturbance Rejection Control of Flexible Joint System

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