Adaptive levitation control for characteristic model of low speed maglev vehicle

Chen, Chen; Xu, Junqi; Ji, Wen; Chao, Ruijie; Sun, Yougang

doi:10.1177/0954406219899677

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…(3) Equation ( 3) model is used as a transfer function model to look up values from KP, KI, and KD [20]. From the results that appear will produce a graph.…”

Section: A Matlab Simulation Testingmentioning

confidence: 99%

PID Control of Magnetic Levitation (Maglev) System

Seto

Ma’arif

2023

jfsc

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Magnetic levitation is a process of drifting or shifting an object with a certain reference, by creating a repulsive force from the gravity of an object caused by an electromagnetic field without noise. In modern times like today, with the density of community activities, especially those living in urban areas, it is necessary to be able to carry out activities efficiently as well as quickly. Therefore, the reason for many people to be able to find an innovative solution to make it easier for humans to carry out their activities. One of the innovations that can be a solution to the limited space and time is in the development of magnetic levitation technology. The system that will be used in controlling this maglev is to use a PID controller. Using this PID control will be tested with several , , and values with a setpoint of 950. At the initial tunning with the values =2, =0.01, and =2. Then in the second test using the values =3, =0.1, and =0.3. Software testing is also carried out using matlab. The exact PID value for controlling an elevated object can be found by changing the value of the , , and values that can be searched through a matlab software in the PID tunner feature, which can then be seen the PID tunning results by analyzing the graph that will be displayed by the PID tunner.

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“…(3) Equation ( 3) model is used as a transfer function model to look up values from KP, KI, and KD [20]. From the results that appear will produce a graph.…”

Section: A Matlab Simulation Testingmentioning

confidence: 99%

PID Control of Magnetic Levitation (Maglev) System

Seto

Ma’arif

2023

jfsc

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“…In the development of the industrial field in recent years, predictive control has been widely used in IT, electric power, aerospace, automobile, and other fields because of its wide application range of predictive model, rolling-horizon, and good control effect [ 1 , 2 , 3 , 4 ]. In 1987, Clarke and Mohtadi propose the generalized predictive control algorithm (GPC) [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization Based on Entropy and Its Application in Implicit Generalized Predictive Control

Zhang

Zhai

Han

et al. 2021

Entropy

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Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advantages of high precision and fast convergence speed in solving constraint problems. In order to effectively avoid the problems of premature and slow operation in the later stage, combined with the idea of the entropy of system (SR), a new weight attenuation strategy and local jump out optimization strategy are introduced into PSO. The velocity update mechanism is cancelled, and the algorithm is adjusted respectively in the iterative process and after falling into local optimization. The improved PSO is used to optimize the performance index in predictive control. The combination of PSO and gradient optimization for rolling-horizon improves the optimization effect of the algorithm. The simulation results show that the system overshoot is reduced by about 7.5% and the settling time is reduced by about 6% compared with the implicit generalized predictive control algorithm based on particle swarm optimization algorithm (PSO-IGPC).

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