Neural Network-Based Control and Active Vibration Mitigation in a Fully-Flexible Arm Space Robot under Elastic Base Influence: A Luenberger Observer Approach
Journal Title: Journal of Intelligent Systems and Control - Year 2023, Vol 2, Issue 4
Abstract
This study explores dynamic simulation and integrated control in a space robotic arm system characterized by a fully-flexible arm and an elastic base. The elastic base is modeled as a lightweight spring, and the modal shapes of a simply-supported beam are selected via the assumed mode method to represent the bending vibrations of the flexible arm. Dynamic equations for the system are formulated by integrating Lagrangian mechanics with momentum conservation principles. The approach involves reducing the system into two lower-order subsystems using a dual-time-scale singular perturbation method. The first subsystem, exhibiting slow variation, accounts for the joint's rigid motion, while the second, fast-varying subsystem addresses the vibrations of the base and arm. Estimation of joint velocities is conducted through a Luenberger observer, complemented by the use of an Radial Basis Function (RBF) neural network to approximate parameter uncertainties within the system. This facilitates the control of rigid motion in the slow-varying subsystem. Subsequently, the fast-varying subsystem's vibration is actively controlled based on linear system optimal control theory. Numerical simulations validate the integrated control approach's effectiveness in managing both motion and vibration, demonstrating its potential in enhancing the operational precision and stability of space robot systems.
Authors and Affiliations
Lijiao Zhang, Xiaoyan Yu
Keywords
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- EP ID EP732132
- DOI https://doi.org/10.56578/jisc020402
- Views 61
- Downloads 0
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