scholarly journals Robust Integral State Feedback Using Coefficient Diagram in Magnetic Levitation System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57003-57011 ◽  
Author(s):  
Iswanto Iswanto ◽  
Alfian Ma'arif
Keyword(s):  
State Feedback ◽  
Magnetic Levitation ◽  
Levitation System ◽  
Magnetic Levitation System

scholarly journals Robust Sliding Mode Control of a Magnetic Levitation System: Continuous-Time and Discrete-Time Approaches

2021 ◽  
Author(s):  
Pratik Vernekar ◽  
Vitthal Bandal
Keyword(s):  
Discrete Time ◽  
Feedback Linearization ◽  
State Feedback ◽  
Magnetic Levitation ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Mismatched Uncertainties ◽  
Levitation System ◽  
Full State Feedback ◽  
Magnetic Levitation System

This paper presents three types of sliding mode controllers for a magnetic levitation system. First, a proportional-integral sliding mode controller (PI-SMC) is designed using a new switching surface and a proportional plus power rate reaching law. The PI-SMC is more robust than a feedback linearization controller in the presence of mismatched uncertainties and outperforms the SMC schemes reported recently in the literature in terms of the convergence rate and settling time. Next, to reduce the chattering phenomenon in the PI-SMC, a state feedback-based discrete-time SMC algorithm is developed. However, the disturbance rejection ability is compromised to some extent. Furthermore, to improve the robustness without compromising the chattering reduction benefits of the discrete-time SMC, mismatched uncertainties like sensor noise and track input disturbance are incorporated in a robust discrete-time SMC design using multirate output feedback (MROF). With this technique, it is possible to realize the effect of a full-state feedback controller without incurring the complexity of a dynamic controller or an additional discrete-time observer. Also, the MROF-based discrete-time SMC strategy can stabilize the magnetic levitation system with excellent dynamic and steady-state performance with superior robustness in the presence of mismatched uncertainties. The stability of the closed-loop system under the proposed controllers is proved by using the Lyapunov stability theory. The simulation results and analytical comparisons demonstrate the effectiveness and robustness of the proposed control schemes.

scholarly journals LQR state feedback controller with precompensator for magnetic levitation system

2021 ◽  
Vol 2111 (1) ◽  
pp. 012004
Author(s):  
A Winursito ◽  
G N P Pratama
Keyword(s):  
Steady State ◽  
High Speed ◽  
State Feedback ◽  
Magnetic Levitation ◽  
Feedback Controller ◽  
Steel Ball ◽  
State Feedback Controller ◽  
Wide Range ◽  
Levitation System ◽  
Magnetic Levitation System

Abstract Magnetic levitation system (MLS) is a nonlinear system that attracts the attention of many researchers, especially control engineers. It has wide range of application such as robotics, high-speed transportation, and many more. Unfortunately, it is not a simple task to control it. Here, we utilize state feedback controller with Linear-Quadratic Regulator (LQR) to regulate the position of a steel-ball in MLS. In addition, we also introduce the precompensator to nullify the steady-state errors. The linearized model, controller, and precompensator are simulated using Matlab. The results and simulation verify that the state feedback controller and precompensator succeed to stabilize the position of steel-ball at the equilibrium for 0.1766 seconds and no steady-state errors.

scholarly journals Design of an adaptive state feedback controller for a magnetic levitation system

2020 ◽  
Vol 10 (5) ◽  
pp. 4782
Author(s):  
Omar Waleed Abdulwahhab
Keyword(s):  
Equilibrium Point ◽  
State Feedback ◽  
Magnetic Levitation ◽  
The State ◽  
Feedback Controller ◽  
Pole Placement ◽  
Operating Point ◽  
State Feedback Controller ◽  
Levitation System ◽  
Magnetic Levitation System

This paper presents designing an adaptive state feedback controller (ASFC) for a magnetic levitation system (MLS), which is an unstable system and has high nonlinearity and represents a challenging control problem. First, a nonadaptive state feedback controller (SFC) is designed by linearization about a selected equilibrium point and designing a SFC by pole-placement method to achieve maximum overshoot of 1.5% and settling time of 1s (5% criterion). When the operating point changes, the designed controller can no longer achieve the design specifications, since it is designed based on a linearization about a different operating point. This gives rise to utilizing the adaptive control scheme to parameterize the state feedback controller in terms of the operating point. The results of the simulation show that the operating point has significant effect on the performance of nonadaptive SFC, and this performance may degrade as the operating point deviates from the equilibrium point, while the ASFC achieves the required design specification for any operating point and outperforms the state feedback controller from this point of view.

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.

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