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Muqing Cao1,Xinhang Xu1,Kun Cao1,Lihua Xie1.[en_title][J].Control Theory and Technology,2023,21(3):458~468.[Copy]
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System identification and control of the ground operationmode of a hybrid aerial–ground robot
MuqingCao1,XinhangXu1,KunCao1,LihuaXie1
0
(1 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore)
摘要:
This paper presents an in-depth analytical and empirical assessment of the performance of DoubleBee, a novel hybrid aerial– ground robot. Particularly, the dynamic model of the robot with ground contact is analyzed, and the unknown parameters in the model are identified. We apply an unscented Kalman filter-based approach and a least square-based approach to estimate the parameters with given measurements and inputs at every time step. Real data are collected and used to estimate the parameters; test data verify that the values obtained are able to model the rotation of the robot accurately. A gain-scheduled feedback controller is proposed, which leverages the identified model to generate accurate control inputs to drive the system to the desired states. The system is proven to track a constant-velocity reference signal with bounded error. Simulations and real-world experiments using the proposed controller show improved performance than the PID-based controller in tracking step commands and maintaining attitude under robot movement.
关键词:  Modelling · Parameter identification · Hybrid robots · Robot control
DOI:https://doi.org/10.1007/s11768-023-00162-x
基金项目:
System identification and control of the ground operationmode of a hybrid aerial–ground robot
Muqing Cao1,Xinhang Xu1,Kun Cao1,Lihua Xie1
(1 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore)
Abstract:
This paper presents an in-depth analytical and empirical assessment of the performance of DoubleBee, a novel hybrid aerial– ground robot. Particularly, the dynamic model of the robot with ground contact is analyzed, and the unknown parameters in the model are identified. We apply an unscented Kalman filter-based approach and a least square-based approach to estimate the parameters with given measurements and inputs at every time step. Real data are collected and used to estimate the parameters; test data verify that the values obtained are able to model the rotation of the robot accurately. A gain-scheduled feedback controller is proposed, which leverages the identified model to generate accurate control inputs to drive the system to the desired states. The system is proven to track a constant-velocity reference signal with bounded error. Simulations and real-world experiments using the proposed controller show improved performance than the PID-based controller in tracking step commands and maintaining attitude under robot movement.
Key words:  Modelling · Parameter identification · Hybrid robots · Robot control