Design and control of a variable structure robot

Zhao, Liang; Song, Yuan; Hu, Jikun

doi:10.1177/1729881420903606

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In the modern logistics industry, the development of warehousing logistics, as one of the main forms of logistics, is undoubtedly the main factor affecting the above problems [2]. As the operation of warehousing logistics is inseparable from control, robotics, and other related disciplines, warehousing logistics has become more and more modern and intelligent with the progress and development of these disciplines [3].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Zhao

2022

Journal of Sensors

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In this paper, warehouse logistics robot as the research object, starting from the reality of modern e-commerce logistics industry, proposed a warehousing logistics mobile robot path planning method. Ant colony algorithm is used to plan the forward path of mobile robot in static and dynamic environment of warehouse logistics. The results show that the elite-based strategy improves the global search capability of ACA and eliminates redundant nodes in the path, and the path length of the obtained plan is significantly better than that of traditional ACA, which is reduced by 3.46% and 5.90%, respectively, indicating that the elite-based strategy and the central-point-based smoothing method play their roles. The path length of the robot’s final operation is larger than that of the global path planning, which increases by 1, accounting for 6.67% of the original path length. Therefore, the storage and logistics mobile robot based on ACA-E algorithm has short driving distance and superior obstacle avoidance ability.

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Section: Introductionmentioning

confidence: 99%

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Zhao

2022

Journal of Sensors

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Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Liu,

Zhang,

et al. 2024

Measurement and Control

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Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

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Design and control of a variable structure robot

Cited by 2 publications

References 25 publications

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

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