Identification and Control of PMSM Using Adaptive BP-PID Neural Network

Chu, Fufei; Wang, Zhanshan; Jia, Kaili

doi:10.1007/978-3-642-39068-5_19

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The traditional PID also has the characteristics of simple structure and convenient parameter adjustment, but in the actual project, it can't modify each parameter online, making the control system can 't adjust in real time according to the operation state, and the dynamic performance is poor. So this paper presents a neural network algorithm combined with PID, the system is not only simple to control, but also has the ability of online learning and adaptive [5] . The structure of the adaptive PID controller implemented by the neural network is shown in Fig.…”

Section: Single Neuron Adaptive Pidmentioning

confidence: 99%

Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID

Liu

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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For the speed sensorless control of permanent magnet synchronous motor (PMSM), a design method based on model reference adaptive observer and single neuron PID speed loop was proposed. In this method, the model reference adaptive observer is used to identify the speed online, the Popov hyperstability is used to determine the reference adaptive law of the speed observer, the neural network module is used to control the q-axis current, and the LMS algorithm is selected to modify the weights of the neural network speed loop online. The simulation results show that the model reference adaptive speed observer control of PMSM based on single neuron PID is effective and robust.

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Section: Single Neuron Adaptive Pidmentioning

confidence: 99%

Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID

Liu

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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“…This section refers to the LADRC stability analysis method proposed by Yoo et al (2007) and Cai et al (2013), which analyzes the convergence of ESO and the stability of the control system based on the missile dynamics model. Then, the stability analysis of the closed-loop system embedded in BP neural network is given.…”

Section: Stability Analysis Of Bp-active Disturbance Rejection Control Technology Systemmentioning

confidence: 99%

Adaptive control of missile attitude based on BP–ADRC

Qiao

Meng

Ke³

et al. 2020

AEAT

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Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

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“…Online estimation is a very important necessity for such systems. Extended Kalman Filter, Model-Reference method, Recursive Least Squares method, neural networks, adaptive algorithms, and decoupling control algorithms are of the online methods to estimate the parameters of PMSM [2,10,[13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Permanent Magnet Synchronous Motor Using Orthogonal Projection and Recursive Least Squares Combinatorial Algorithm

Yousefi

Ghanbari

2015

Mathematical Problems in Engineering

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This paper presents parameter estimation of Permanent Magnet Synchronous Motor (PMSM) using a combinatorial algorithm. Nonlinear fourth-order space state model of PMSM is selected. This model is rewritten to the linear regression form without linearization. Noise is imposed to the system in order to provide a real condition, and then combinatorial Orthogonal Projection Algorithm and Recursive Least Squares (OPA&RLS) method is applied in the linear regression form to the system. Results of this method are compared to the Orthogonal Projection Algorithm (OPA) and Recursive Least Squares (RLS) methods to validate the feasibility of the proposed method. Simulation results validate the efficacy of the proposed algorithm.

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Identification and Control of PMSM Using Adaptive BP-PID Neural Network

Cited by 7 publications

References 8 publications

Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID

Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID

Adaptive control of missile attitude based on BP–ADRC

Parameter Estimation of Permanent Magnet Synchronous Motor Using Orthogonal Projection and Recursive Least Squares Combinatorial Algorithm

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