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| System identification and control of the ground operationmode of a hybrid aerial–ground robot |
| MuqingCao1,XinhangXu1,KunCao1,LihuaXie1 |
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| (1 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore) |
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| 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 |
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| 基金项目: |
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| 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 |
