A self-tuning robust full-state feedback control design for the magnetic levitation system

Bıdıklı, Barış; Bayrak, Alper

doi:10.1016/j.conengprac.2018.06.017

Cited by 26 publications

(12 citation statements)

References 19 publications

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“…Remark 2: At this point it should be noted that these type of cascade systems were successfully used in Eroglu and Ablay (2016) and Bidikli and Bayrak (2018) for the control of maglev system. In both of the mentioned studies different type of controllers designed for the control of electromechanical subsystem were combined with PI controller.…”

Section: Control Design and Analysismentioning

confidence: 99%

“…In both of the mentioned studies different type of controllers designed for the control of electromechanical subsystem were combined with PI controller. In this study, PI controller is preferred for the control of electrical subsystem by inspiring from Eroglu and Ablay (2016) and Bidikli and Bayrak (2018).…”

Section: Control Design and Analysismentioning

confidence: 99%

“…An integral joint structure based control strategy was proposed for stabilization of maglev train in Wang et al (2018). A self–tuning robust integral of signum of error (RISE) based controller was designed and used in a cascade control system that was proposed for the control of maglev system in Bidikli and Bayrak (2018). An application of a robust control design based on approximate feedback linearization technique to an electromagnetic levitation system was presented in Oh and Choi (2018).…”

Section: Introductionmentioning

confidence: 99%

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An observer-based adaptive control design for the maglev system

Bıdıklı

2020

Transactions of the Institute of Measurement and Control

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In this study, a nonlinear adaptive controller that can be used to control a magnetic levitation (maglev) is designed. The designed controller is equipped with a nonlinear velocity observer to provide the control without measuring velocity. Its capability to adaptively compensate all parametric uncertainties during the control process is one of the main advantages of this controller. Utilizing this capability, control of the maglev system can be realized without using any knowledge about system parameters. Due to the fast convergence capability of the designed observer and the desired model dependent structure of the adaptation rules, the proposed control design provides better performance than most of the robust and adaptive controllers that have been frequently used to control maglev system. The observer dynamics are analyzed via a Lyapunov–like preliminary analysis. Then, convergence of the observation and the tracking errors under the closed–loop operation and stability of the closed–loop error dynamics are proven via a Lyapunov–based stability analysis where the result obtained in the mentioned preliminary analysis is used. Performance of the designed observer–controller couple is demonstrated via experimental results. The efficiency of the designed controller is tested against a robust proportional–integral–derivative (PID) controller and an another Lyapunov–based nonlinear robust controller called as robust integral of sign of error (RISE) controller. Experimental results show that the designed controller performs the best tracking performance with the least control effort among these three controllers.

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Section: Control Design and Analysismentioning

confidence: 99%

Section: Control Design and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An observer-based adaptive control design for the maglev system

Bıdıklı

2020

Transactions of the Institute of Measurement and Control

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“…Hence, MLS offers several advantages such as working in a high vacuum environment, reduction in noise, and high accuracy positioning system [7]. MLS uses the principle of electromagnetism to levitate ferromagnetic objects at a certain position within air-space [8].…”

Section: Introductionmentioning

confidence: 99%

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

Ismael¹,

Qasim²,

Noaman³

2021

JESA

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Magnetic Levitation System (MLS) objective is to levitate objects to the desired height without any contact. MLS is highly nonlinear and inherently unstable. Such a system imposes a challenge when designing robust and high-performance controllers. This paper presents the design of a Sliding Mode (SM) controller with an Integral term called SM-I controller to achieve the desired levitation against nonlinearities and uncertainties of the system. The controller parameters are tuned using the Equilibrium Optimizer (EO) algorithm. The Effectiveness of the proposed controller is validated by simulation results. Simulations are performed for servo tracking with and without perturbations in the MLS parameters. The proposed controller is compared with the conventional SM, LQR, and PID controllers to show its superiority. The results prove that the SM-I is more efficient than the other controllers.

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“…Another method was used to design PID controller for stabilizing maglev system is IMC [2]. In [3], a self-tuning robust integral of signum of error (RISE) based controller is designed and used to control a maglev system. The controller is tested on an experimental maglev system and successful results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Tuning Parameters of Fuzzy Logic Controller using PSO for Maglev System

Truong¹,

Ngo²

2019

IJCA

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The paper proposes to use Particle Swarm Optimization (PSO) to tune parameters of a fuzzy logic controller for regulating a magnetic levitation (maglev) system at a desired position. PSO is a meta-heuristic search method. This method is inspired by bird flocking behavior searching for food. In this study, the rule base of the Fuzzy Logic Controller (FLC) is brought by expert experience, and the parameters of the controller including the membership function parameters and scaling gains will be optimally tuned by the PSO such that a quadratic criterion is minimized. Simulation results show that the designed fuzzy controller is able to stabilize the position of the maglev system. Besides, a state feedback controller is also used to regulate the maglev system. Although, the simulation results show that FLC gives performance better than the state feedback controller but the latter is more robust.

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A self-tuning robust full-state feedback control design for the magnetic levitation system

Cited by 26 publications

References 19 publications

An observer-based adaptive control design for the maglev system

An observer-based adaptive control design for the maglev system

Equilibrium Optimizer-Based Robust Sliding Mode Control of Magnetic Levitation System

Tuning Parameters of Fuzzy Logic Controller using PSO for Maglev System

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