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Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, Vol. 63 No. 3, 2022.

Izvorni znanstveni članak

https://doi.org/10.1080/00051144.2022.2052398

Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

Fengxia Xu ; College of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, People’s Republic of China
Xinyu Zhang ; College of Computer and Control Engineering, Qiqihar University, Qiqihar, People’s Republic of China
Zhongda Lu ; College of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, People’s Republic of China
Shanshan Wang ; College of Computer and Control Engineering, Qiqihar University, Qiqihar, People’s Republic of China

Puni tekst: engleski pdf 2.431 Kb

str. 463-473

preuzimanja: 188

citiraj

Sažetak

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification.

Ključne riječi

U-model; particle swarm identification; extended Kalman filtering; neural network control

Hrčak ID:

287517

URI

https://hrcak.srce.hr/287517

Datum izdavanja:

11.4.2022.

Posjeta: 459 *