Helicopter Dynamic Model Identification by Conditional Attitude Hold Logic

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

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On robot dynamic model identification through sub-workspace evolved trajectories for optimal torque estimation

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2013.6696689 ◽

2013 ◽

Cited By ~ 1

Author(s):

Nicola Pedrocchi ◽

Enrico Villagrossi ◽

Federico Vicentini ◽

Lorenzo Molinari Tosatti

Keyword(s):

Dynamic Model ◽

Model Identification ◽

Torque Estimation

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Dynamic Model Identification of PAM-Based Rehabilitation Robot Using Neural MIMO NARX Model

IFMBE Proceedings - The Third International Conference on the Development of Biomedical Engineering in Vietnam ◽

10.1007/978-3-642-12020-6_10 ◽

2010 ◽

pp. 39-43 ◽

Cited By ~ 1

Author(s):

Ho Pham Huy Anh ◽

Le Tan Loi

Keyword(s):

Dynamic Model ◽

Model Identification ◽

Rehabilitation Robot ◽

Narx Model

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Omnidirectional Mobile Robot Dynamic Model Identification by NARX Neural Network and Stability Analysis Using the APLF Method

Symmetry ◽

10.3390/sym12091430 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1430

Author(s):

Liang Xin ◽

Yuchao Wang ◽

Huixuan Fu

Keyword(s):

Neural Network ◽

Stability Analysis ◽

Mobile Robot ◽

Dynamic Model ◽

Complex Dynamics ◽

Model Identification ◽

Theoretical Method ◽

Omnidirectional Mobile Robot ◽

Path Dependent ◽

Narx Neural Network

In this paper, the NARX neural network system is used to identify the complex dynamics model of omnidirectional mobile robot while rotating with moving, and analyze its stability. When the mobile robot model rotates and moves at the same time, the dynamic model of the mobile robot is complex and there is motion coupling. The change of the model in different states is a kind of symmetry. In order to solve the problem that there is a big difference between the mechanism modeling motion simulation and the actual data, the dynamic model identification of mobile robot in special state based on NARX neural network is proposed, and the stability analysis method is given. To verify that the dynamic model of NARX identification is consistent with that of the mobile robot, the Activation Path-Dependent Lyapunov Function (APLF) algorithm is used to distinguish the NARX neural network model expressed by LDI. However, the APLF method needs to calculate a large number of LMIs in practice and takes a lot of time, and, to solve this problem, an optimized APLF method is proposed. The experimental results verify the effectiveness of the theoretical method.

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