Modeling and Control of Two Wheels Robot Using Linear Quadratic Regulators

Ahmed, Ausama Hadi; Aburas, Othman E.; Meelad, Nouralddeen A.; Abu-Raas, Alhasan A.

doi:10.1109/mi-sta52233.2021.9464494

Cited by 1 publication

(1 citation statement)

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“…The problem with this technique is that it does not provide transient and training datasets considering only a single operating condition. System identification for optimal control design (linear quadratic regulator) using the SI toolbox in Matlab is presented in [20]. The experimental modeling of human heart rate response during walking, cycling and rowing at various intensity levels is presented in [21].…”

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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Section: Introductionmentioning

confidence: 99%