Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Truong, Thanh Nguyen; Vo, Anh Tuan; Kang, Hyejin

doi:10.3390/s22239132

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…Various approaches known from control theory have been used for this purpose (root locus [ 19 , 20 ], state space [ 21 ], disturbance rejection control [ 22 ], fuzzy control [ 23 ], sliding mode control [ 24 ], fuzzy sliding mode control [ 25 ], robust control [ 26 ], neural network control [ 27 ], various nonlinear approaches [ 28 , 29 ]). The authors of [ 30 ] deal with dynamical uncertainties and exterior perturbations in a magnetic levitation system using a real-time prescribed performance control. This allows for chattering reduction and faster convergence to the equilibrium point.…”

Section: Materials and Methods: The Magnetic Levitation Systemmentioning

confidence: 99%

Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm

Reznichenko

Podržaj

2023

Sensors

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Multi-objective (MO) optimization is a developing technique for increasing closed-loop performance and robustness. However, its applications to control engineering mostly concern first or second order approximation models. This article proposes a novel MO algorithm, suitable for the design and control of mechanical systems, which does not require any order reduction techniques. The controller parameters are determined directly from a special type of rapid analysis of simulated transient responses. The case study presented in this article consists of a magnetic levitation system. Certain difficulties such as the nonlinearity identification of the magnetic force and duo magnetic field sensor scheme were addressed. To point out the advantages of using the developed approach, the simulations as well as the experiments performed with the help of the created algorithm were compared to those made with common MO algorithms.

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Section: Materials and Methods: The Magnetic Levitation Systemmentioning

confidence: 99%

Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm

Reznichenko

Podržaj

2023

Sensors

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“…PPC is a technique that is designed to ensure transient and steady-state performance in controlled systems, with limited tracking errors, convergence rate, and maximum overshoot [42][43][44][45][46]. To establish specific performance boundaries, a PPF is typically used.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

Truong

Kang

2023

Mathematics

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This paper proposes a fixed-time neural network-based prescribed performance control method (FNN-PPCM) for robot manipulators. A fixed-time sliding mode controller (SMC) is designed with its strengths and weaknesses in mind. However, to address the limitations of the controller, the paper suggests alternative approaches for achieving the desired control objective. To maintain stability during a robot’s operation, it is crucial to keep error states within a set range. To form the unconstrained systems corresponding to the robot’s constrained systems, we apply modified prescribed performance functions (PPFs) and transformed errors set. PPFs help regulate steady-state errors within a performance range that has symmetric boundaries around zero, thereby ensuring that the tracking error is zero when the transformed error is zero. Additionally, we use a singularity-free sliding surface designed using transformed errors to determine the fixed-time convergence interval and maximum allowable control errors during steady-state operation. To address lumped uncertainties, we employ a radial basis function neural network (RBFNN) that approximates their value directly. By selecting the transformed errors as the input for the RBFNN, we can minimize these errors while bounding the tracking errors. This results in a more accurate and faster estimation, which is superior to using tracking errors as the input for the RBFNN. The design procedure of our approach is based on fixed-time SMC combined with PPC. The method integrates an RBFNN for precise uncertainty estimation, unconstrained dynamics, and a fixed-time convergence sliding surface based on the transformed error. By using this design, we can achieve fixed-time prescribed performance, effectively address chattering, and only require a partial dynamics model of the robot. We conducted numerical simulations on a 3-DOF robot manipulator to confirm the effectiveness and superiority of the FNN-PPCM.

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A fixed-time sliding mode control for uncertain magnetic levitation systems with prescribed performance and anti-saturation input

Vo,

Truong,

Kang

et al. 2024

Engineering Applications of Artificial Intelligence

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Real-Time Implementation of the Prescribed Performance Tracking Control for Magnetic Levitation Systems

Cited by 9 publications

References 43 publications

Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm

Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

A fixed-time sliding mode control for uncertain magnetic levitation systems with prescribed performance and anti-saturation input

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