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 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.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Influence of exercise modality and modality-specific training on endurance exercise performance in hypoxia

2017 Fall.Includes bibliographical references.INTRODUCTION: In hypoxia, endurance exercise performance is impaired. The magnitude of impairment may be variable between individuals and exercise modalities. The purpose of this study was to determine the influence of exercise modality and modality-specific training on hypoxia-mediated performance decrements. METHODS: In a randomized cross-over design, endurance trained cyclists (4 males, 3 females) and rowers (5 males, 3 females) performed exercise on both cycling and rowing ergometers. On separate occasions, participants completed graded exercise tests in normoxia (FiO2= 0.21), and standardized exercise (15 minutes, 100 W) and time trials (4 km cycling, 2 km rowing) in normoxia and hypoxia (FiO2= 0.15). RESULTS: Hypoxia-mediated performance decrements were not different between cyclists and rowers (17±1 vs. 18±1%, p=0.189), cycling and rowing (18±2 vs. 16±2%, p=0473), or any combination of training or test modality (p=0.138). In rowers, peripheral oxygen saturation (SpO2) was lower at the end of rowing compared to cycling time trials (78±1 vs. 83±1%, p=0.002), and lower than that of cyclists at the end of rowing time trials (78±1 vs. 83±1%, p<0.001). DISCUSSION: Hypoxia-mediated performance decrements were not different between training modalities, test modalities, or any combination of the factors. We speculate that reduced SpO2 in rowers at the end of rowing time trials may be related to a greater active muscle mass, causing a rightward shift in the oxyhemoglobin dissociation curve and reduced transit time of blood in pulmonary capillaries. In conclusion, SpO2 may be related to active muscle mass during exercise and could potentially modulate performance in hypoxia