To analyse walking, running or hopping motions, models with high degrees of freedom are usually used. However simple reductionist models are advantageous within certain limits. In a simple manner, the hopping motion is generally modelled by a spring-mass system, resulting in piecewise smooth dynamics with marginally stable periodic solutions. For a more realistic behaviour, the spring is replaced by a variety of muscle models due to which asymptotically stable periodic motions may occur. The intrinsic properties of the muscle model, i.e. preflexes, are usually taken into account in three complexities-constant, linear and Hill-type. In this paper, we propose a semi-closed form feed-forward control which represents the muscle activation and results in symmetrical hopping motion. The research question is whether hopping motions with symmetric force-time history have advantages over asymmetric ones in two aspects. The first aspect is its applicability for describing human motion. The second aspect is related to robotics where the efficiency is expressed in term of performance measures. The symmetric systems are compared with each other and with those from the literature using performance measures such as hopping height, energetic efficiency, stability of the periodic orbit, and dynamical robustness estimated by the local integrity measure (LIM). The paper also demonstrates that the DynIn MatLab Toolbox that has been developed for the estimation of the LIM of equilibrium points is applicable for periodic orbits.
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Sci Rep
January 2025
Department of Electrical Engineering, Chosun University, Gwangju, 61452, South Korea.
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December 2024
Physical Therapy Department, Rehabilitation Faculty, Tehran University of Medical Sciences, Tehran, Iran.
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View Article and Find Full Text PDFPLoS One
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Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Okhla, New Delhi, India.
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View Article and Find Full Text PDFHeliyon
September 2024
State Grid Shanxi Electric Power Research Institute, Taiyuan, Shanxi, China.
This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.
View Article and Find Full Text PDFISA Trans
November 2024
The College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110819, China. Electronic address:
The distributed optimal design of high-speed train movement is systematically investigated in this article. A distributed optimal control law is proposed, addressing the train consist of cars coupled by spring buffers, and is affected by aerodynamic drag and rolling resistance. A new distributed controller is proposed to decouple the train model by fully removing the in-train force, which greatly simplifies the complexity of calculation.
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