Efficient System Identification of a Two-Wheeled Robot (TWR) Using Feed-Forward Neural Networks

System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x and y and the angle of rotat...

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Bibliographic Details
Published in:Electronics (Basel) 2022-11, Vol.11 (21), p.3584
Main Authors: Khan, Muhammad Aseer, Baig, Dur-e-Zehra, Ali, Husan, Ashraf, Bilal, Khan, Shahbaz, Wadood, Abdul, Kamal, Tariq
Format: Article
Language:English
Subjects:
Angular velocity
Artificial neural networks
Control systems
D C motors
Datasets
Dynamical systems
Electric motors
Identification
Kinematics
Mobile robots
Neural networks
Neurons
Nonlinear dynamics
Parameter estimation
Perturbation
Regression analysis
Robotics
Robots
Simulation
System identification
Time series
Velocity
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Summary:System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x and y and the angle of rotation Ψ along the z-axis by giving a set of input vectors in terms of linear velocity ‘V’ (i.e., generated through the angular velocity ‘ω’ of a DC motor). The DC motor rotates the TWR’s wheels that have a wheel radius of ‘r’. Training datasets are achieved via simulating nonlinear kinematics of the TWR in a MATLAB Simulink environment by varying the linear scale sets of ‘V’ and ‘(r ± ∆r)’. Perturbation of the TWR’s wheel radius at ∆r = 10% is introduced to cater to the robustness of the TWR wheel kinematics. A trained ANN accurately modeled the kinematics of the TWR. The performance indicators are regression analysis and mean square value, whose achieved values met the targeted values of 1 and 0.01, respectively.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11213584