Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Bojan-Dragoş, Claudia-Adina; Precup, Radu‐Emil; Tomescu, Marius-Lucian; Preitl, Ștefan; Tanasoiu, Oana-Maria; Hergane, Stefania

doi:10.15837/ijccc.2017.5.2770

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…5. The general linearized state-space mathematical model can be applied in this situation by accepting u 2 = 0, as follows [46]:…”

Section: Parametermentioning

confidence: 99%

“…The operating points in Equation ( 6) are selected in order to cover the typical operating conditions within the steady-state area of the ball position feedback input-output map and eliminate the input-output map's extreme values due to potential instability [46]. P (1) (0.0063, 0, 1.22, 0.39), P (2) (0.007, 0, 1.145, 0.39), P (3) (0.0077, 0, 1.07, 0.39), P (4) (0.0098, 0, 0.89, 0.39), P (5) (0.009, 0, 0.9345, 0.39), ( 6)…”

Section: Parametermentioning

confidence: 99%

“…The unstable and nonlinear mathematical model of the process is linearized at seven operating points. The authors of [46] presented a gain-scheduling control design procedure for classical proportionalintegral-derivative controllers (PID-GS-C) for a positioning system. The method was applied to a laboratory magnetic levitation system with two electromagnets (MLS2EM), which allowed for several experimental verifications of the proposed solution.…”

Section: Introductionmentioning

confidence: 99%

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Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

Almobaied,

Al-Nahhal,

Arrieta

et al. 2023

Mathematics

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This study focuses on the design of a robust PD gain-scheduling controller (PD-GS-C) for an unstable SISO (single-input, single-output) magnetic levitation system with two electromagnets (MLS2EM). Magnetic levitation systems offer various advantages, including friction-free, reliable, fast, and cost-effective operations. However, due to their unstable and highly nonlinear nature, these systems require sophisticated feedback control techniques to ensure optimal performance and functionality. To address these challenges, in this study, we derive the nonlinear state-space mathematical model of the MLS2EM and linearize it around five different operating points. The PD-GS-C controller aims to stabilize the system and improve steady-state control error. The strategy for obtaining the PD controller gains involves a parameter space technique, which specifies performance requirements. This technique results in ranges of proportional (KP) and derivative (KD) gains that are used by the PD-GS-C structure. To optimize the controller’s performance further, we utilize the big bang–big crunch optimization technique (BB-BC) to determine the optimal PD gains within the specified ranges. The optimization process focuses on achieving optimal performance in terms of a specific performance index function. This function quantifies the system’s time-domain step response criteria, which include minimizing overshoot percentage, settling time, and rising time. The index function is inversely proportional to the desired performance criteria, meaning that the goal is to maximize the index function to optimize the system’s performance. To validate the effectiveness and viability of the proposed strategy, we conducts MATLAB simulations and real-time experiments. The simulations and experimental findings serve to demonstrate the controller’s performance and verify its capabilities in stabilizing the MLS2EM magnetic levitation system.

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“…5. The general linearized state-space mathematical model can be applied in this situation by accepting u 2 = 0, as follows [46]:…”

Section: Parametermentioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

Almobaied,

Al-Nahhal,

Arrieta

et al. 2023

Mathematics

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“…As the proportional-integral-derivative (PID) controller, which is a famous controller that always applied in the industrial applications, many researchers have trying to investigated and modified this controller with the integration of various kind of methods, including the modification of the structure in this controller, for instance the gain-scheduling control, or the fractional order (FO-PID) controller that is proven to be more effective in its performance, compared with the conventional PID controller [3][4][5][6], which have been applied to the hydraulic system [7][8][9]. Researchers also attempt to combine this controller, the conventional PID controller with the computational optimization method [10][11][12][13][14][15], and also the FOPID with the computational optimization method [16,17], applied to the EHA system.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Fractional Order Pid and Sliding Mode Control with Optimization Tuning Access

2019

IJRTE

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The statistical analyses in the past showing the important properties of the electrohydraulic actuator (EHA) system, especially in the growth of the world economy. Dealing with the existing drawback in the EHA system, various types of control schemes have been introduced in the past. In this paper, to produce a more insightful view of the performance and the capabilities of the controller, three different types of controllers have been designed and compared. The favourite controller in the industry field, which is the proportional-integral-derivative (PID) controller will be first introduced. Follow by the improved PID controller, named Fractional Order (FO-PID) controller will be designed. Then, the prominent robust controller in the control field, called sliding mode controller (SMC) will be established. Instead of obtaining the controller’s parameters without any appropriate technique, the well-known tuning technique in computer science, named particle swarm optimization (PSO) will be utilized. Referring to the performances produced by these controllers, it can be concluded that the SMC is capable to generate most desired control performance that produced the highest accuracy with the smallest error in the analyses.

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“…point and the equilibrium point (the point that the system was linearized about) increases. To handle this problem, sliding mode controller (SMC) [23][24][25], adaptive SMC [26], PID-notch filters [27], and linearization-gain scheduling controller PID controller [28], linearization-gain scheduling PI controller [29], and linearization-adaptive PD controller [30] were designed to provide robustness against operating point variation. This paper proposes an ASFC to stabilize the MLS, where the controller parameters become a function of the operating point, and pole placement method is used to design the controller.…”

mentioning

confidence: 99%

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

Abdulwahhab

2020

IJECE

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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.

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Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Cited by 6 publications

References 33 publications

Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

Performance Evaluation of Fractional Order Pid and Sliding Mode Control with Optimization Tuning Access

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

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