Stability analysis of feedback linearized magnetic levitation system using sum-of-squares method

Tandon, Bhawna; Kaur, Sukhdeep; Kalra, Ashima

doi:10.1109/ispcc.2017.8269715

Cited by 3 publications

(3 citation statements)

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“…Fuzzy logic controller for magnetic levitation system shows better performance than PID controller when adding mass as disturbance [4]. Then, feedback linearized magnetic levitation system using Sum of Squares (SOS) method explain about the stability of maglev, based on calculations using Lyapunov obtained negative definite or the system was globally asymptotically stable [5]. However, this study did not explain the output response in graphical form.…”

Section: Introductionmentioning

confidence: 88%

“…However, this paper did not consider disturbance in magnetic levitation. In addition, there are also other methods such as fuzzy logic controller [4], feedback linearization [5], LQR [6], neural network [7][8] [9]. Fuzzy logic controller for magnetic levitation system shows better performance than PID controller when adding mass as disturbance [4].…”

Section: Introductionmentioning

confidence: 99%

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Sliding Mode Control with Gain Scheduled for Magnetic Levitation System

Uswarman

Istiqphara

Nugroho

2019

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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This paper presents a study of controlling magnetic levitation object using conventional sliding mode control (CSMC) and sliding mode control (SMC) with gain-scheduled. SMC with gain-scheduled aims to improve the robustness of the control system from disturbance. The CSMC simulation results show that output can follow the set point if there is no interference, but if the disturbance happens then there is overshoot and undershoot of 0.034 mm and 0.07 mm for disturbance 10 * sin( ) and 20 * sin( ). Then SMC with gain-scheduled shows excellent performance because the output can follow the reference even if it got disturbance of 10 * sin( ) and 20 * sin( ).

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control with Gain Scheduled for Magnetic Levitation System

Uswarman

Istiqphara

Nugroho

2019

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…The essential idea of feedback linearization is to decouple a nonlinear system into a pseudo-linear system by the mean of nonlinear state feedback, and then use a linear controller to deal with the pseudo-linear system. However, among all kinds of cases [22][23][24][25] that apply feedback linearization to maglev systems, most of them focused on the individual control of single-DoF magnetic levitation, i.e., decentralized control. However, since there is coupling between the degrees of freedom in multi-DoF maglev systems, decentralized control tends not to cope well with multi-DoF magnetic levitation systems [26].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Non-Contact Tension Testing Device Based on Magnetic Levitation

Ren

Oka

2022

IEEE Access

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This paper proposed a non-contact tension testing device using magnetic levitation technology, where a specimen can be tested while being levitated. To guarantee the alignment of tension force, the magnetic characteristics of three shapes of floators were investigated via electromagnetic analysis, and a ring floator was selected according to the electromagnetic analysis results. Also, the number of the coil turns, and the dimensions of all the device parts were minimized while ensuring sufficient magnetic force. Furthermore, to realize the levitation of the system, which belongs to two-degree of freedom (2-DoF) magnetic levitation system, a nonlinear mathematic model was established, and a centralized feedback linearization control algorithm was proposed. A tuning method for the centralized feedback linearization control algorithm was proposed based on control simulation results. The control simulation results implied that the centralized feedback linearization control algorithm allows the device to withstand the disturbances caused by the tension force even in the case of mismatches between the controller and the plant. Moreover, a model for estimating specimen elongation was developed employing a support vector machine (SVM). Also, the estimation simulations were performed, and the estimation results demonstrated that the range of the estimation error was between -0.1988mm and 0.2269mm, the root mean square error (RMSE) and coefficient of determination (R 2 ) were 0.0843mm and 98.76% respectively. Ultimately, a levitation experiment and a tension experiment were successfully performed, the levitation experiment results demonstrated that the proposed tuning method is effective and the centralized feedback linearization control algorithm has stronger robustness to step disturbance than the traditional linear control algorithm. The tension experiment results indicated that the whole control system cope well with an increasing tension force.

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