Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Vinodh Kumar, Elumalai"'
Publikováno v:
Journal of Control Science and Engineering, Vol 2020 (2020)
This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objec
Externí odkaz:
https://doaj.org/article/580943379a1a43669e4323391f57110a
Publikováno v:
In Applied Soft Computing April 2016 41:77-90
Publikováno v:
IETE Journal of Research. :1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e25cd64d64e98884921a333231046145
https://doi.org/10.1080/03772063.2022.2039079
https://doi.org/10.1080/03772063.2022.2039079
Autor:
Vinodh Kumar Elumalai, Raaja Ganapathy Subramanian, Joshua Sunder David Reddipogu, Soundarya Srinivasan, Shantanu Agrawal
Publikováno v:
Archives of Electrical Engineering, Vol vol. 67, Iss No 2 (2018)
This paper presents an enhanced internal model control (EIMC) scheme for a time-delayed second order unstable process, which is subjected to exogenous disturbance and model variations. Even though the conventional internal model control (IMC) can pro
Externí odkaz:
https://doaj.org/article/c8af6b8c70654afa9a46360db9aeffd1
Publikováno v:
Transactions of the Institute of Measurement and Control. :014233122311691
To address the nonlinear stabilization problem and improve the tracking control feature of ball on plate system (BPS), this paper puts forward a novel Takagi Sugeno (TS) fuzzy control augmented with the current cycle feedback iterative learning contr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bee001aba4eaac47e5a5f274a53585f2
https://doi.org/10.1177/01423312231169163
https://doi.org/10.1177/01423312231169163
Publikováno v:
International Journal of Systems Science. 52:1042-1060
This paper presents an iterative learning control (ILC) scheme augmented with the feedback control for solving the nonlinear stabilisation and tracking control problem of ball on plate system, whic...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b80f0596a476efb4fb63cb6cde8f5c0
https://doi.org/10.1080/00207721.2020.1853273
https://doi.org/10.1080/00207721.2020.1853273
Publikováno v:
Transactions of the Institute of Measurement and Control. 42:2969-2983
This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59269f4deeaf7606fa0c3e10c69293ef
https://doi.org/10.1177/0142331220936584
https://doi.org/10.1177/0142331220936584
Current cycle feedback iterative learning control for tracking control of magnetic levitation system
Publikováno v:
Transactions of the Institute of Measurement and Control. 42:543-550
This paper presents the current cycle feedback iterative learning control (CCF-ILC) augmented with the modified proportional integral derivative (PID) controller to improve the trajectory tracking and robustness of magnetic levitation (maglev) system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf9d98c99b1bb81604e818f10f26ed6f
https://doi.org/10.1177/0142331219877052
https://doi.org/10.1177/0142331219877052
Publikováno v:
Advances in Automation, Signal Processing, Instrumentation, and Control ISBN: 9789811582202
To improve the trajectory tracking and robustness of closed-loop servo system against the model perturbation, this paper presents a novel norm optimal iterative learning control (NOILC) scheme combined with proportional velocity (PV) feedback control
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c6b0b4b0f8a1ff85514c3f339aa7539
https://doi.org/10.1007/978-981-15-8221-9_170
https://doi.org/10.1007/978-981-15-8221-9_170
Publikováno v:
Advances in Automation, Signal Processing, Instrumentation, and Control ISBN: 9789811582202
Iterative Learning Control (ILC) aim is to improve control performance through iterations. ILC enhances transient behavior of the system, without knowing entire dynamics of plant model. Applied in feedforward path, it learns from past iteration and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7415b43173e905c0424dab2e799b8b9d
https://doi.org/10.1007/978-981-15-8221-9_132
https://doi.org/10.1007/978-981-15-8221-9_132