Control of a magnetic levitation system using feedback linearization

Uswarman, Rudi; Cahyadi, Adha Imam; Wahyunggoro, Oyas

doi:10.1109/ic3ina.2013.6819156

Cited by 17 publications

(5 citation statements)

References 9 publications

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“…To distinguish the parameters in the plant and the controller, the parameters a and c in the controller will be written as ac and cc., while the parameters a and c in the plant will be remained same as before. Therefore, Eq (22) is rewritten as follows:…”

Section: Parametersmentioning

confidence: 99%

“…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%

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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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Section: Parametersmentioning

confidence: 99%

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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“…There are some proposed methods to control the maglev system such as PID controller [9], the fuzzy logic controller [10], [11], LQR [12], Fault-tolerant control and state observer [13], nonlinear power shaping [14], sliding mode control [15], Global Sliding Mode Control [16], Modified Sliding Mode Control [17], feedback linearization [18] and backstepping [19]. Each of them has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Robust Integral State Feedback Using Coefficient Diagram in Magnetic Levitation System

Iswanto

Ma’arif

2020

IEEE Access

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Magnetic Levitation System or Maglev system is a modern and future technology that has many advantages and applications. Its characteristic is highly nonlinear, fast dynamics, and unstable, so it is challenging to make a suitable controller. The model of the Maglev system is in nonlinear state-space representation, and then feedback linearization is implemented to obtain the linear model system. Then, the integral state feedback control that tuned by the coefficient diagram method is implemented. The robustness of the controller is determined using the coefficient diagram method. The result of the standard coefficient diagram parameter will be compared with the robustness parameter. The open-loop test simulation showed that the maglev system has a nonlinear characteristic. Among all of the uncertainties, the uncertainty of resistance provides the highest nonlinearity, even by the small value of uncertainty. The examination of the mass, inductance, and resistance uncertainties showed that the robustness parameter is able to handle them and to provide a robust controller. INDEX TERMS State feedback, robust control, coefficient diagram method, magnetic levitation, integral control.

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“…Intuitively, the instability of magnetic systems lies in the fact that magnetic attraction or repulsion increases or decreases in relation to the square of distance. Thus, most control strategies for Maglev Systems make use of servo-mechanisms [31] and a feedback linearization [30] around a particular operating point of the complex nonlinear differential equations [46] describing the sophisticated mechanical and electrical dynamics. Despite their intrinsic complexity, these systems have exhibited utility in numerous contexts and in particular Maglev System have generated considerable scientific interest in transportation due to their ability to minimize mechanical loss, allow faster travel [18], minimize mechanical vibration, and emit low levels of noise [16].…”

Section: Brief Introductionmentioning

confidence: 99%

Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems

Xiang

Lopez

Musau

et al. 2018

Safe, Autonomous and Intelligent Vehicles

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Neural networks have been widely used to solve complex realworld problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the behaviors of neural network systems will be crucial for their applications in safety-critical systems. In this paper, the reachable set estimation and verification problems for Nonlinear Autoregressive-Moving Average (NARMA) models in the forms of neural networks are addressed. The neural network involved in the model is a class of feed-forward neural networks called Multi-Layer Perceptron (MLP). By partitioning the input set of an MLP into a finite number of cells, a layer-by-layer computation algorithm is developed for reachable set estimation for each individual cell. The union of estimated reachable sets of all cells forms an over-approximation of reachable set of the MLP. Furthermore, an iterative reachable set estimation algorithm based on reachable set estimation for MLPs is developed for NARMA models. The safety verification can be performed by checking the existence of intersections of unsafe regions and estimated reachable set.Several numerical examples are provided to illustrate our approach.

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Control of a magnetic levitation system using feedback linearization

Cited by 17 publications

References 9 publications

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

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

Robust Integral State Feedback Using Coefficient Diagram in Magnetic Levitation System

Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems

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