Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System

Lin, Faa-Jeng; Teng, Li-Tao; Shieh, Po-Huang

doi:10.1109/tie.2007.894710

Cited by 75 publications

(22 citation statements)

References 22 publications

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“…These systems have natural unstable nonlinear dynamics requiring closed-loop control designs for stabilization. Several control techniques have been applied to the stabilization of MAGLEV systems, such as I/O linearization [16,17], backstepping [18], and sliding mode control [19], among others. The sliding mode control [10] has been extensively used in electromechanical systems due to its robustness to unknown bounded perturbations.…”

Section: Application To the Control Of A Magnetic Levitation Systemmentioning

confidence: 99%

Design of a Mathematical Unit in FPGA for the Implementation of the Control of a Magnetic Levitation System

Raygoza-Panduro

Ortega-Cisneros

Rivera

et al. 2008

International Journal of Reconfigurable Computing

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This paper presents the design and implementation of an automatically generated mathematical unit, from a program developed in Java that describes the VHDL circuit, ready to be synthesized with the Xilinx ISE tool. The core contains diverse complex operations such as mathematical functions including sine and cosine, among others. The proposed unit is used to synthesize a sliding mode controller for a magnetic levitation system. This kind of systems is used in industrial applications requiring high level of mathematical calculations in small time periods. The core is designed to calculate trigonometric and arithmetic operations in such a way that each function is performed in a clock cycle. In this paper, the results of the mathematical core are shown in terms of implementation, utilization, and application to control a magnetic levitation system.

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Section: Application To the Control Of A Magnetic Levitation Systemmentioning

confidence: 99%

Design of a Mathematical Unit in FPGA for the Implementation of the Control of a Magnetic Levitation System

Raygoza-Panduro

Ortega-Cisneros

Rivera

et al. 2008

International Journal of Reconfigurable Computing

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“…In [14], a state feedback controller was proposed to navigate a small magnetized robot in the vertical direction with 16m accuracy. Lin in [17] proposed using a combination of traditional sliding-mode control system and a radial basis function network estimator for positioning the levitated object of a magnetic levitation system. The sliding-mode controller was used to provide basic levitation of the object.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Motion Control of a Magnetically Navigated Microrobotic System

Zhang

Khamesee

2016

Proceedings of the 3rd International Conference of Control, Dynamic Systems, and Robotics (CDSR'16)

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-This paper presents a technology for remotely navigating a magnetized microrobot in three-degree-of-freedom (3-DOF) using magnetic energy. The magnetic energy source is a magnetic drive unit that consists of six iron-core electromagnets, a soft-iron yoke, and a soft-iron pole-piece. The dynamic models of the magnetically navigated microrobot were derived from the magnetic field model in the workspace. An experimental method was applied to define the magnetic field model. Based on the derived dynamic models, a feed-forward plus standard PID controller was used to control the motion of the microrobot with 3-DOF. Experiment was conducted to validate the performance of the proposed controller. The experimental results showed 20µm motion accuracy in 3-DOF in a motion range of 10 × 10 × 30mm 3 .Keywords: Magnetic navigation, Microrobot, Magnetic field, PID plus feedforward controller IntroductionMicromanipulation involves performing tasks using macro scale end-effectors that have micro motion accuracy, and using micro scale end-effectors directly. For the first case, the end-effector of a micromanipulator has a mechanical arm connecting it to its base. This type of micromanipulator includes lead-screw driving stages and piezoelectric actuators [1,2,3]. However, the mechanical connection produces frictions, vibration, and backlash that degrade the performance of micromanipulation. For the second case, an end-effector is remotely manipulated without any mechanical connection to the base of a micromanipulator. This type of micromanipulator is capable of working in hazard and out-of-reach environments. Magnetic levitation micromanipulators, having remotely manipulated micro end-effector and micrometer motion accuracy, are most frequently studied and have promising potential in biomedical applications [4,5,6,7,8].Active magnetic navigation of a microrobot requires a controlled magnetic field source. Generally, electromagnets are used for multi-dimensional manipulation of a microrobot [9,10,11,12]. The magnetic field generated by electromagnets is controlled by a current. The multi-degree of motion freedom of a robot is achieved by setting up special configurations of multi-electromagnets. Since the strength of the magnetic field decays rapidly as the distance from electromagnet increases, an iron-core is commonly used to significantly enhance the magnetic field strength outside the electromagnet. This enhanced magnetic field can then expand the workspace of the microrobot.Modeling the magnetic field in the microrobots workspace is crucial to developing a magnetic navigation system. For systems that have air-core coils and permanent magnets produced magnetic field, analytical methods such as Bio-Savart Law and Amperes Law are applied to find a closed-form magnetic field model. However, for systems that have iron-core electromagnets produced magnetic field, the iron results in high nonlinearity in the whole system. Therefore, it is difficult to find a closed-form for modeling the magnetic field in the workspace. Al...

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“…SMC approach has been widely used for control design problem [6,7]. SMC is one of the effective nonlinear robust control approaches since it provides desired system dynamics with an invariance property to uncertainties once the system dynamics are controlled in the sliding mode [8][9][10][11]. SMC is designed so that the system trajectories move onto a sliding surface in a finite time and tends to an equilibrium point along this surface [12].…”

Section: Introductionmentioning

confidence: 99%

Virtual laboratory for sliding mode and PID control of rotary inverted pendulum

Demirtaş

Altun

İstanbullu

2010

Comp Applic In Engineering

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This paper presents a new virtual laboratory tool which teaches sliding mode control (SMC) and proportional-integral-derivative (PID) control to graduate students. Additionally, it describes performance differences between two control methods graphically. This educational virtual laboratory tool contains the control of rotary inverted pendulum. This system is a typical example of nonlinear and under-actuated systems and also well-known in control engineering for practicing different control theories. At first, the nonlinear dynamic equations of the inverted pendulum are presented. Then a virtual laboratory tool is designed for SMC and PID. After that, the results are analyzed. The validity of designed tool is verified by an experiment.

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Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System

Cited by 75 publications

References 22 publications

Design of a Mathematical Unit in FPGA for the Implementation of the Control of a Magnetic Levitation System

Design of a Mathematical Unit in FPGA for the Implementation of the Control of a Magnetic Levitation System

Modeling and Motion Control of a Magnetically Navigated Microrobotic System

Virtual laboratory for sliding mode and PID control of rotary inverted pendulum

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