Two wheel self-balanced mobile robot identification based on experimental data

Shahraki, Mohsen; Sh, Mahdi Aliyari; Mousavinia, Amir

doi:10.1109/iraniancee.2017.7985163

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…The feedforward neural network was able to predict the tilt angle with a very low mean square error (MSE). Ref [15] considers two phases for system identification. First, the system is considered a multi-input multi-output (MIMO) system where the currents for both motors are the inputs, and the velocities of both wheels are the output.…”

Section: Gray-box Modelingmentioning

confidence: 99%

Modeling and Parameters Estimation of a Self-Balancing Two-Wheeled Vehicle

Hassan,

Moustafa,

Moness

2024

Journal of Advanced Engineering Trends

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The self-balancing two-wheeled vehicle is a practical realization of the well-established control benchmark of the inverted pendulum system. Despite the sophisticated dynamics and kinematics of the inverted pendulum system, it has acquired great interest in real-life applications starting from Segway and hoverboards to the self-balancing wheelchair. These applications benefit from the dynamical structure that provides high maneuverability within narrow spaces. However, the system complexity regarding the high nonlinearity and instability requires accurate models for model-based control of the balancing and motion planning control objectives. This work addresses the modeling and the parameters estimation of a lab-scale version of the self-balancing two-wheeled vehicle. First, a nonlinear dynamical model based on LaGrange kinematics is developed. Then, real-time datasets for closed-loop operations are acquired for offline optimization. Finally, an optimization problem is formulated and solved for the parameter estimation through a decoupled block-by-block approach. The obtained grey-box model is validated for reliable and accurate fitting of the real system. The obtained model was able to simulate the realtime collected data with reasonable meaningful estimated parameters.

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Section: Gray-box Modelingmentioning

confidence: 99%

Modeling and Parameters Estimation of a Self-Balancing Two-Wheeled Vehicle

Hassan,

Moustafa,

Moness

2024

Journal of Advanced Engineering Trends

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“…Thus, there is a need for an approach that can estimate the dynamics of a system based on experimental observation. The results of several linear and nonlinear models for two-wheeled robots are compared in [18]. The identification of TWR dynamics using neural networks is presented in [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of a Nonlinear Two-Wheeled Robot Using Data-Driven Approach

et al. 2022

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A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

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“…To reduce complexity when designing Nonlinear Control Systems, we adopted a system identification using the black-box model and trained the ANN as the estimated model of a TWR dynamic using datasets acquired from the simulation studies of the nonlinear dynamics of a TWR. System identification of a two-wheeled, self-balancing robot was performed using the experimental datasets in two different phases [14]. This study compares linear models such as ARX, ARMAX, Bl, and OE with nonlinear models such as Wiener and Hammerstein.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Two wheel self-balanced mobile robot identification based on experimental data

Cited by 8 publications

References 9 publications

Modeling and Parameters Estimation of a Self-Balancing Two-Wheeled Vehicle

Modeling and Parameters Estimation of a Self-Balancing Two-Wheeled Vehicle

Dynamic Modeling of a Nonlinear Two-Wheeled Robot Using Data-Driven Approach

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

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