Computation Of Stabilizing PID Controllers For Magnetic Levitation System With Parametric Uncertainties

Almobaied, Moayed; Al-Nahhal, Hassan S.; Issa, Khaled B.A.

doi:10.1109/icepe-p51568.2021.9423481

Cited by 3 publications

(9 citation statements)

References 8 publications

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“…Traditional techniques, on the other hand, will supply designers with only one set of PID parameter values (𝐾 𝑃 ,𝐾 𝐼 , and 𝐾 𝐷 ). A parameter space approach, on the other hand, is a graphical strategy for locating all stability zones of PID parameters and is considered a strong tool for robust stabilization challenges [15]. The open loop uncertain transfer function is shown in (36).…”

Section: Robust Stabilization Pid Controller Designmentioning

confidence: 99%

“…PID controller is one of the most common and straightforward control strategies used in various applications due to its simplicity and effectiveness. However, one of the greatest drawbacks of the PID controller is the tuning process for its gains (𝐾 𝑃 , 𝐾 𝐼 , and 𝐾 𝐷 ) [15]. Indeed, exact input-output feedback linearization and sliding mode control using Lyapunov functions are two promising methods for nonlinear control.…”

Section: Introductionmentioning

confidence: 99%

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Design a Robust Optimal Proportional-Integral-Derivative Controller for CE152 Magnetic Levitation System Using Bee Colony Algorithm

Al-Nahhal,

Almobaied

2024

PJBAS

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One of the most popular methods for giving feedback to the control loop in industrial control systems is the proportional-integral-derivative (PID) controller. The tuning of the PID controller, however, is currently being researched by engineers. In this research, a robust PID controller is proposed for the CE152 magnetic levitation system. Magnetic levitation, commonly referred to as maglev, is a technology that uses magnetic fields to levitate an object, such as a vehicle or train, above a track. By using magnetic forces to counteract gravitational and inertial forces, maglev systems can achieve frictionless movement and potentially higher speeds compared to conventional wheeled transportation. In this research, the robust PID controller is involved by computing all stabilized PID controller gains for the affine linear characteristic polynomial in the presence of uncertain parameters using the parameter space approach and the edge theorem. The results of the parameter space approach are ranges of PID gains (𝐾 𝑃 , 𝐾 𝐷 , 𝐾 𝐼 ). Here, the optimal PID gains were chosen by the Artificial Bee Colony optimization algorithm to get optimal performance for CE152 magnetic levitation. The research defines a specific performance index function that quantifies the system's time-domain step response criteria (small overshoot percentage with significant minimization of both settling and rising times). This index function is inversely proportional to the desired performance criteria, aiming to optimize the system's performance. MATLAB simulations are used to validate and demonstrate the efficiency of the proposed graphical method for enhancing stability in the maglev system.

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Section: Robust Stabilization Pid Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design a Robust Optimal Proportional-Integral-Derivative Controller for CE152 Magnetic Levitation System Using Bee Colony Algorithm

Al-Nahhal,

Almobaied

2024

PJBAS

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“…In [22] The authors use the parameter space strategy to calculate all stabilizing PID parameters (KP, KI, and KD) for the magnetic levitation ED-4810 system with parametric uncertainties. The authors of this article apply the parameter space technique to modify parameters of the classical PID controller (KP, KI, and KD) that are considered to be uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Robust-PID controller design for magnetic levitation system using parameter space approach

Almobaied¹,

Al-Nahhal²,

Issa³

2023

World J. Adv. Eng. Technol. Sci.

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Recently, implementation of air magnetic suspension systems has become more challenging due to the growing demand for lightweight technologies, comfortable cabins, vehicle safety stability, and pollution control. Magnetic levitation, or Maglev, is the method of propelling a target into the air by adjusting different magnetic forces. The absence of contact and the avoidance of wear and friction phenomena are key considerations in the applications of magnetic levitation technology. Due to the high nonlinearity in the modelling process of such kind of systems, the stabilizing of magnetic levitation has been considered as a challenging task for many researchers in control engineering sector. The computation of all stabilizing PID gains controllers for the magnetic levitation benchmark ED-4810 (Maglev) is demonstrated in this paper using two different scenarios. In the first one, the tuning parameters of the classical PID controller (KP, KI, and KD) are assumed to be the uncertain parameters. The second scenario demonstrates the uncertainty in the transfer function of the system by using the resistance and the inductance as uncertain parameters. The characteristic polynomial of the linearized uncertain model is shown to be an unstable affine polynomial using the Zero Exclusion Theorem and the singular frequencies technique. The parameter space approach is used to illustrate the values of all PID parameters in order to achieve robust stability in the two scenarios. The effectiveness of the presented graphical technique has been verified through MATLAB simulation to obtain robust stability for magnetic levitation systems.

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“…Magnetic levitation systems are controlled as SISO system architectures, and just one electromagnet in the simplest and most common arrangement [3]. Such systems (MLS1EM) utilize magnetic forces created by providing the electromagnet and one-axis distance sensor with the necessary voltage [4]. In more complex arrangements (MLS2EM), a secondary electromagnet can be used to produce an external gravitational pull that is distinct from that produced by the first [5].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous efforts have recently been published in the literature to control magnetic levitation systems. Input-output and input-state linearization approaches are among the feedback linearization-based controllers that have been developed [7][8][9][10][11][12], in addition to the design of linear state feedback controls [13], observer-based controls [14,15], cognitive strategies [16][17][18], slide mode control [19][20][21], backstepping control [22], model-based predictive control (MPC) [23,24], adaptive H-infinity tracking control [25], and proportionalintegral-derivative (PID) control [4,26]. In addition, there are several adaptive control schemes that have already been presented in the past few years for positioning control schemes and magnetic levitation technologies.…”

Section: Introductionmentioning

confidence: 99%

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

Almobaied,

Al-Nahhal,

Arrieta

et al. 2023

Mathematics

Self Cite

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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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Computation Of Stabilizing PID Controllers For Magnetic Levitation System With Parametric Uncertainties

Cited by 3 publications

References 8 publications

Design a Robust Optimal Proportional-Integral-Derivative Controller for CE152 Magnetic Levitation System Using Bee Colony Algorithm

Design a Robust Optimal Proportional-Integral-Derivative Controller for CE152 Magnetic Levitation System Using Bee Colony Algorithm

Robust-PID controller design for magnetic levitation system using parameter space approach

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

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