Identification of Vehicle Dynamics Model and Lever-Arm for Arbitrarily Mounted Motion Sensor

Hwang, Y. S.; Jeong, Yongseop; Kweon, In-So; Choi, Sungdo

doi:10.1109/jsen.2021.3139120

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…From a data perspective, in some methods, called asymptotic methods, the model parameters are proved to converge in an asymptotic sense [6] hence theoretically requiring infinite amounts of data. Asymptotic methods are usually formulated in a statistical framework like the maximum likelihood estimators (MLE), the prediction error method (PEM), and various versions of Kalman filters that are used for model parameters' estimation [7]. The infinite amounts of data required by asymptotic methods motivated the interest in system identification techniques with finite sample complexity, like the non-asymptotic methods that require finite amounts of data with guarantees on error bounds.…”

Section: A Literature Reviewmentioning

confidence: 99%

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

Peringal,

Chehadeh,

Boiko

et al. 2024

IEEE Sensors J.

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In this paper, we present a real-time system identification method based on relay feedback testing with applications to multirotor unmanned aerial vehicles. The proposed identification method provides an alternative to the expensive lab testing of certain unmanned aerial vehicle (UAV) dynamic parameters. Moreover, it has the advantage of identifying the parameters that get changed throughout the operation of the UAV which requires onboard identification methods. The modified relay feedback test (MRFT) is used to generate stable limit cycles at frequency points that reveal the underlying UAV dynamics. The locus of the perturbed relay system (LPRS) is used to predict the exact amplitude and frequency of these limit cycles. Real-time identification is achieved by using the homogeneity properties of the MRFT and the LPRS which are proven in this paper. The proposed identification method was tested experimentally to estimate the aerodynamic parameters as well as the onboard sensor's time delay parameters. The MRFT testing takes a few seconds to perform, and the identification computations take an average of 0.2 seconds to complete in modern embedded computers. The proposed identification method is compared against stateof-the-art alternatives. Advantages in identification accuracy and quantification of uncertainty in estimated parameters are shown.

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Section: A Literature Reviewmentioning

confidence: 99%

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

Peringal,

Chehadeh,

Boiko

et al. 2024

IEEE Sensors J.

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Bidirectional dual closed-loop trajectory tracking control based on chassis model identification

Dai,

Wang,

Yang

et al. 2024

J. Phys.: Conf. Ser.

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The field of trajectory tracking control for ground vehicles has problems such as unknown control object manipulation characteristics, missing dynamic digital models, and susceptibility to distortion of upper-layer tracking control algorithms. This paper first proposes a method for identifying the chassis forward/inverse dynamics model based on vehicle-level state variables and control variable methods to solve these issues. Secondly, the control object manipulation characteristics and dynamic fitting digital model are obtained based on a specific electric drive-tracked chassis. Thirdly, a linear quadratic regulator is used as the core path tracking algorithm, and a dual closed-loop trajectory tracking control algorithm based on the inverse dynamics fitting model is established for both lateral and longitudinal position feedforward + velocity feedback correction. Finally, systematic verification is conducted using the Simulink simulation environment and real vehicles. The verification results show that the proposed model identification method supports vehicle-level state fitting accuracy maintained within 3%, and the trajectory tracking control algorithm maintains tracking accuracy within 0.1m under extreme U-turn conditions. The dual closed-loop control structure enhances control accuracy and robustness.

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Information-Aided Inertial Navigation: A Review

Engelsman,

Klein

2023

IEEE Trans. Instrum. Meas.

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Identification of Vehicle Dynamics Model and Lever-Arm for Arbitrarily Mounted Motion Sensor

Cited by 3 publications

References 52 publications

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

Relay-Based Identification of Aerodynamic and Delay Sensor Dynamics With Applications for Unmanned Aerial Vehicles

Bidirectional dual closed-loop trajectory tracking control based on chassis model identification

Information-Aided Inertial Navigation: A Review

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