Adaptive impedance control for the hydraulic manipulator under the uncertain environment

Ding, Ruqi; Wang, Jianhui; Cheng, Min; Sun, Mingxuan; Xu, Bing; Wang, Zhen

doi:10.1007/s40430-023-04323-6

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…In [11], a robust observer-based trajectory tracking control method was presented to solve the stable motion problem for an unmanned aerial manipulator considering internal interactions and external disturbances. In [12], an adaptive controller was proposed for a hydraulic manipulator considering uncertain environment with unknown stiffness and location. However, the above results rarely explored the presence of random disturbances in the robot workspace.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

Dang,

2024

Preprint

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This study presents a novel event-triggered finite-time stochastic control method for a robot manipulator. The random moment of inertia of the manipulator system is expressed by building a stochastic dynamic model, and the parameter variation disturbance is estimated by using a stochastic configuration neural network. An event-triggered controller with uncertain disturbance rejection is proposed, which not only realizes the stochastic finite-time stability of the tracking error system, but also guarantees the safety of motion velocity, and robustly improves the tracking accuracy of the robot manipulator. Compared with the existing works, the obvious feature of the proposed method is that it can simultaneously solve the random disturbance and uncertain parameter disturbance of the manipulator system, save communication resources, and ensure that the manipulator system can reach a steady state in finite time. We also discuss the effectiveness of the proposed stochastic tracking control method. Simulation and comparative analysis results further show that the controller can be updated less frequently while guaranteeing robust tracking performance of the robot manipulator.

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Section: Introductionmentioning

confidence: 99%

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

Dang,

2024

Preprint

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Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

Dang,

2024

Nonlinear Dyn

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A Novel Impedance-Based Parallel Cooperative Control Method for Front and Rear Landing Gear Hydraulic Systems of UAVs

Qiu,

Wang,

Shi

et al. 2024

Electronics

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Cargo handling issues affect the ability of large heavy-duty Unmanned Aerial Vehicles (UAVs) to transport cargo and limit the development of large UAVs. Compared to conventional landing gear, hydraulically controlled landing gear can tilt the drone within a specified angle, facilitating smoother loading and unloading of goods. Therefore, it is important to study the hydraulic landing gear control system for a UAV to make the UAV’s tilt possible. In this paper, an impedance-based parallel cooperative control method for front and rear landing gear hydraulic systems of large heavy-duty UAVs is presented, which can achieve UAV tilting within a reasonable angle during the loading and unloading of cargoes by large, heavy-duty UAVs. This paper establishes the physical model of the UAV’s landing gear, the mathematical model of the hydraulic system, and the kinematic model of the airframe. Through kinematic analysis, the correlation between each hydraulic dive unit’s (HDU’s) extension length in the landing gear and the UAV’s tilt angle is established. This paper introduces a two-fold based-loop parallel control technique, featuring angle based-loop control for the UAV’s front and position based-loop control for its rear landing gear. It aims to enable the UAV to freely tilt for loading and unloading cargo at a predetermined angle, by measuring the UAV’s tilting angle, the HDU’s force exerted on the landing gear, and its positional parameters. Ultimately, the practicality of this technique is confirmed through simulations and experiments.

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Adaptive impedance control for the hydraulic manipulator under the uncertain environment

Cited by 3 publications

References 31 publications

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

A Novel Impedance-Based Parallel Cooperative Control Method for Front and Rear Landing Gear Hydraulic Systems of UAVs

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