Study on two feedback linearization control methods for the magnetic suspension system

Cui, Peng; Guo, Zhaoyu; Li, Jie

doi:10.1109/chicc.2015.7259780

Cited by 4 publications

(1 citation statement)

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“…MLBS is a modern technology that has high efficiency and frictionless characteristics [2]. This technique is applied to various systems, such as Suspension [3], Wind Turbine [4], Microbots [5], Bearing [6], Medical [7] and Vehicles [8]. The latest and famous application of MLBS is a Magnetic Levitation (Maglev) train [9].…”

Section: Introductionmentioning

confidence: 99%

CDM Based Servo State Feedback Controller with Feedback Linearization for Magnetic Levitation Ball System

Ma’arif

Cahyadi

Wahyunggoro

2018

International Journal on Advanced Science, Engineering and Information Technology

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This paper explains the design of Servo State Feedback Controller and Feedback Linearization for Magnetic Levitation Ball System (MLBS). The system uses feedback linearization to change the nonlinear model of magnetic levitation ball system to the linear system. Servo state feedback controller controls the position of the ball. An integrator eliminates the steady state error in servo state feedback controller. The parameter of integral gain and state feedback gains is achieved from the concept of Coefficient Diagram Method (CDM). The CDM requires the controllable canonical form, because of that Matrix Transformation is needed. Hence, feedback linearization is applied first to the MLBS then converted to a controllable form by a transformation matrix. The simulation shows the system can follow the desired position and robust from the position disturbance. The uncertainty parameter of mass, inductance, and resistance of MLBS also being investigated in the simulation. Comparing CDM with another method such as Linear Quadratic Regulator (LQR) and Pole Placement, CDM can give better response, that is no overshoot but a quite fast response. The main advantage of CDM is it has a standard parameter to obtain controller's parameter hence it can avoid trial and error.

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