scholarly journals Design of a 2-DOF Control and Disturbance Estimator for a Magnetic Levitation System

2017 ◽  
Vol 7 (1) ◽  
pp. 1369-1376 ◽  
Author(s):  
A. Pati ◽  
V. C. Pal ◽  
R. Negi
Keyword(s):  
Control Strategy ◽  
Magnetic Levitation ◽  
Open Loop ◽  
Loop Control ◽  
Set Point ◽  
Point Tracking ◽  
Load Disturbance ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Disturbance Estimator

This work proposes a systematic two-degree freedom control scheme to improve the reference input tracking and load disturbance rejection for an unstable magnetic levitation system. The proposed control strategy is a two-step design process. Firstly, a proportional derivative controller is introduced purposely to get the desired set-point response of the magnetic levitation system and then, an integral square error (ISE) performance specification is used for designing a set-point tracking controller. Secondly, a disturbance estimator is designed using the desired closed loop complimentary sensitivity function for the rejection of load disturbances. This leads to the decoupling of the nominal set-point response from the load disturbance response similar to an open loop control manner. Thus, it is convenient to optimize both controllers simultaneously as well as separately. The effectiveness of the proposed control strategy is validated through simulation.

Implementation and Nonlinear Stabilization of a MIMO Magnetic Levitation System

2011 ◽  
Author(s):  
Jiaji Zhang ◽  
Xuesong Mei ◽  
Dongsheng Zhang ◽  
Yun Zhang ◽  
Jian Sun
Keyword(s):  
Disturbance Rejection ◽  
Control Algorithm ◽  
Magnetic Levitation ◽  
Sliding Mode ◽  
Double Loop ◽  
Terminal Sliding Mode ◽  
Loop Control ◽  
Disturbance Rejection Control ◽  
Levitation System ◽  
Magnetic Levitation System

This paper presents the implementation of a three degree-of-freedom magnetic levitation system. First the dynamic model of the magnetic levitation is developed. Then based on the nonlinear model, a robust nonlinear double-loop control algorithm is applied to stabilize the system. The double-loop control architecture consists of two components: 1) terminal sliding mode control (TSMC) is employed in the outer loop to stabilize the rigid dynamic model while maintains robustness.2) Auto disturbance rejection control (ADRC) is applied in the inner loop as a current loop controller to track current command. Finally, experimental results are presented to illustrate the performance of the system dynamic response and current response in each coil. The experiment results show that the terminal sliding mode algorithm combined with auto disturbance rejection control algorithm is effective in the nonlinear MIMO magnetic levitation system.

scholarly journals Model predictive control of a magnetic levitation system using two-level state feedback

2020 ◽  
Vol 53 (5-6) ◽  
pp. 962-970
Author(s):  
Zhenlin Zhang ◽  
Yonghua Zhou ◽  
Xin Tao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
State Feedback ◽  
Magnetic Levitation ◽  
Air Gap ◽  
State Variables ◽  
Maglev Train ◽  
Levitation System ◽  
Magnetic Levitation System

The magnetic levitation system is a critical part to guarantee safe and reliable operations of a maglev train. In this paper, the control strategy is proposed for the magnetic levitation system based on the model predictive control incorporating two-level state feedback. Taking advantage of the measurable state variables, that is, air gap, electromagnet acceleration, and control current through high-resolution sensor measurement, the first-level nonlinear state feedback is to linearize the unstable nonlinear magnetic levitation system, and the second-level linear state feedback is to further stabilize the system and improve the dynamic performances, which together provide a stable prediction model. The simulation results demonstrate that the proposed control strategy can ensure high-precision air gap control and favorable disturbance resistance ability.

scholarly journals Control of a MIMO Magnetic Levitation System using Exponential Control Barrier Function

2020 ◽  
Author(s):  
Caio I. G. Chinelato ◽  
Bruno A. Angélico
Keyword(s):  
Feedback Linearization ◽  
Control Structure ◽  
Magnetic Levitation ◽  
Metal Plate ◽  
Open Loop ◽  
Control Law ◽  
Barrier Functions ◽  
Levitation System ◽  
Safety Constraints ◽  
Magnetic Levitation System

This work presents the control of a magnetic levitation system. The system is constituted by a Y shape metal plate that must be levitated by electromagnetic attractive forces. The system is nonlinear, open loop unstable and Multiple-Input/Multiple-Output (MIMO), whose inputs are represented by attractive forces generated from three electromagnets and outputs are represented by three plate positions. The proposed control structure uses Quadratic Programming (QP) to combine performance/stability objectives, represented by an arbitrary nominal control law, and safety constraints, represented by Control Barrier Functions (CBFs). The arbitrary nominal control law applied is determined by feedback linearization. Multiple safety constraints with relative-degree greater than one were applied. One way to deal with this is to use Exponential Control Barrier Functions (ECBFs). The results of this control structure applied to the magnetic levitation system are obtained through numerical simulations and indicate that performance/stability objectives are reached and safety constraints are respected.

scholarly journals Research on Neural Network PID Quadratic Optimal Controller in Active Magnetic Levitation

2014 ◽  
Vol 8 (1) ◽  
pp. 42-47
Author(s):  
Zhongqiao Zheng ◽  
Xiaojing Wang ◽  
Yanhong Zhang ◽  
Jiangsheng Zhang
Keyword(s):  
Neural Network ◽  
Magnetic Levitation ◽  
Learning Rule ◽  
Open Loop ◽  
Optimal Controller ◽  
Positioning Errors ◽  
Levitation System ◽  
Dynamic Performances ◽  
Magnetic Levitation System ◽  
Neural Network Pid

In response to the uncertainty, nonlinearity and open-loop instability of active magnetic levitation control system, a neural network PID quadratic optimal controller has been designed using optimum control theory. By introducing supervised Hebb learning rule, constraint control for positioning errors and control increment weighting are realized by adjusting weighting coefficients, using weighed sum-squares of the control increment and the deviation between actual position and equilibrium position of the rotor in active magnetic levitation system as objective function. The simulation results show that neural network PID quadratic optimal controller can maintain the stable levitation of rotor by effectively improving static and dynamic performances of the system, so as to maintain the stable levitation of rotor in active magnetic levitation system which has stronger anti-jamming capacity and robustness.

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