2016
DOI: 10.1109/tia.2015.2465939
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Input–Output Feedback Linearization Control With On-Line MRAS-Based Inductor Resistance Estimation of Linear Induction Motors Including the Dynamic End Effects

Abstract: This paper proposes the theoretical framework and the consequent application of the input-output Feedback Linearization (FL) control technique to Linear Induction Motors (LIM). LIM, additionally to Rotating Induction Motor (RIM), presents other strong non-linearities caused by the dynamic end effects, leading to a space-vector dynamic model with timevarying inductance and resistance terms and a braking force term. This paper, starting from a recently developed dynamic model of the LIM taking into consideration… Show more

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Cited by 89 publications
(41 citation statements)
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“…Based on the nonlinear control theory presented in Appendix A, and in conjunction with Equations (2), (3), (5), and (6), we can establish a transformational matrix that varies with time to linearize the nonlinear system under a particular condition [42][43][44]. Then, the state feedback controller is projected onto the linear system to accomplish dynamic tracking.…”
Section: Augmented-state Feedback Linearization (Afl) Control For Nonmentioning
confidence: 99%
“…Based on the nonlinear control theory presented in Appendix A, and in conjunction with Equations (2), (3), (5), and (6), we can establish a transformational matrix that varies with time to linearize the nonlinear system under a particular condition [42][43][44]. Then, the state feedback controller is projected onto the linear system to accomplish dynamic tracking.…”
Section: Augmented-state Feedback Linearization (Afl) Control For Nonmentioning
confidence: 99%
“…Nonlinear control algorithms are divided into three main categories: model-based control algorithms, soft computing (artificial intelligence)-based control theory and hybrid control procedures. The main advantage of a nonlinear model-free controller is system knowledge, which has been improved by researchers over the years [2,[4][5][6][7]. Although it has several advantages, this method has challenges associated with system reliability and robustness.…”
Section: Introductionmentioning
confidence: 99%
“…The feedback linearization technique (FLC), back-stepping control algorithm (BSC), passivity-based control (PBC), Lyapunov-based algorithm (PBC) and sliding mode control (SMC) are popular methods for designing model-based controllers. However, selecting a suitable control technique is a major challenge for many researchers [4][5][6][7][8][9].…”
Section: Introductionmentioning
confidence: 99%
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“…In [11], an optimized adaptive tracking control is applied for a LIM considering the uncertainties. In [12], the authors use input-output feedback linearization control technique with online model reference adaptive system (MRAS) method suiting the induction resistance to realize the velocity following goal, whereas the three mentioned methods are highly dependent on the accuracy of the model. Once the model is improperly defined or the system parameters cannot be accurately obtained, the dynamic response of the system will hardly be satisfied.…”
Section: Introductionmentioning
confidence: 99%