Indirect vector control of induction motor using fuzzy sliding mode controller

Kar, Biranchi Narayan; Mohanty, Kanungo Barada; Singh, Madhu; Choudhury, Satish

doi:10.1109/eeeic.2012.6221446

Cited by 6 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, controller's parameters must be continuously adapted to tackle the aforementioned issue. Various adaptive and robust control approaches such as: self tuning PI control scheme [5], Hybrid Control (HC) strategy [6], Sliding Mode Control (SMC) scheme [7], [8], Fuzzy Logic Control (FLC) [9], [10], and Artifical Neural Network (ANN) [11], [12], will be designed. To avoid the inherent undesirable features of conventional control schemes ANN is used.…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant speed regulation of Induction Motor using Artifical Neural Network

Zeb

Farhana

Mehmood

et al. 2015

2015 International Conference on Emerging Technologies (ICET)

View full text Add to dashboard Cite

This paper successfully develops and investigates the implementation of Artificial Neural Network (ANN) based robust speed control strategy for Indirect Vector Control (IVC) three phase Induction Motor (1M) drive. The 1M is modeled in terms of dq in s y nchronousl y rotating reference frame for IVC in Matlab/Simulink. The main purpose of the proposed design is to accomplish robustness for load disturbances, speed variation, parameter uncertainties, and electrical faults. The performance of aforesaid control technique is compared with that of conventional tuned PI control scheme. Simulation results of the ANN guarantee effectiveness and robustness regarding overshoot, undershoot, rise time, fall time and chattering for different operating condition in comparison to traditional PI control scheme.

show abstract