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RISS 인기검색어
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- 저자 : Venkatesan Nithya (검색결과 2 건)
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Venkatesan Nithya,Rathinasamy Sakthivel,Faris Alzahrani,마용기 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.11
This paper investigates the problem of robust decentralized fault-tolerant resilient control for fractionalorder large-scale interconnected uncertain system, and the problem considered here is subject to mixed H∞ and passivity performance constraint, external disturbances, controller perturbations and control input saturation. Based on the Lyapunov approach, the sufficient conditions are derived in terms of linear matrix inequalities to ensure the asymptotic stabilization of the fractional-order large-scale system with a prespecified mixed H∞ and passivity performance index. The main objective of this work is to design a robust decentralized fault-tolerant resilient controller which compensates both actuator fault and input saturation in its design for obtaining the required result. Finally, a numerical example is included to illustrate the effectiveness of the designed control law. The simulation results reveal that our proposed controller not only can effectively deal with actuator faults, but also has very good robustness for input saturation and external disturbances.
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Optimization of a horizontal axis marine current turbine via surrogate models
Thandayutham, Karthikeyan,Avital, E.J.,Venkatesan, Nithya,Samad, Abdus Techno-Press 2019 Ocean systems engineering Vol.9 No.2
Flow through a scaled horizontal axis marine current turbine was numerically simulated after validation and the turbine design was optimized. The computational fluid dynamics (CFD) code Ansys-CFX 16.1 for numerical modeling, an in-house blade element momentum (BEM) code for analytical modeling and an in-house surrogate-based optimization (SBO) code were used to find an optimal turbine design. The blade-pitch angle (${\theta}$) and the number of rotor blades (NR) were taken as design variables. A single objective optimization approach was utilized in the present work. The defined objective function was the turbine's power coefficient ($C_P$). A $3{\times}3$ full-factorial sampling technique was used to define the sample space. This sampling technique gave different turbine designs, which were further evaluated for the objective function by solving the Reynolds-Averaged Navier-Stokes equations (RANS). Finally, the SBO technique with search algorithm produced an optimal design. It is found that the optimal design has improved the objective function by 26.5%. This article presents the solution approach, analysis of the turbine flow field and the predictability of various surrogate based techniques.
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