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 Robust state feedback controller design of STATCOM using chaotic optimization algorithm

2010 ◽  
Vol 7 (2) ◽  
pp. 253-268 ◽  
Author(s):  
Amin Safari ◽  
Hossein Shayeghi ◽  
Ali Heidar
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
State Feedback ◽  
Controller Design ◽  
Low Frequency ◽  
Operating Conditions ◽  
Feedback Controller ◽  
Static Synchronous Compensator ◽  
State Feedback Controller ◽  
Wide Range

In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM) using Chaotic Optimization Algorithm (COA) is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.

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