Command Filtering-Based Neural Network Control for Fractional-Order PMSM With Input Saturation

Lu, Senkui; Wang, Xingcheng

doi:10.1109/access.2019.2942958

Cited by 10 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on RBFNN, an adaptive backstepping control method was proposed by Shahvali et al 25 for the consensus problem of fractional order nonlinear multi-agent systems. For fractional order input saturated permanent magnet synchronous motor (PMSM), Lu and Wang 26 proposed a NN method combined with command filtering technique. Sui et al 27 used NN to model uncertain fractional order systems and presented an adaptive switching dynamic surface control method for fractional order non-strict feedback nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

View full text Add to dashboard Cite

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

show abstract

Section: Introductionmentioning

confidence: 99%

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Chen

Cao

Yuan

et al. 2021

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…26,27 Conversely, one drawback of backstepping algorithms that can engender complexity is called an "explosion of complexity" due to the repeated time derivative of the virtual control input, which should be solved in complex applications. 28 Hence, Lu and Wang 29 proposed the use of a fractional-order command filter for PMSMs to address this issue, and a command filtering-based neural network control scheme was developed to handle input saturation in PMSMs. They, in another paper, 30 studied observer-based command-filtered adaptive neural network (NN) tracking control for a fractional-order chaotic PMSM subject to parameter uncertainties and external load disturbances.…”

Section: Introductionmentioning

confidence: 99%

Robust decomposed system control for an electro‐mechanical linear actuator mechanism under input constraints

Heydari Shahna,

Bahari,

Mattila

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper aims to develop a robust decomposed system control (RDSC) strategy under input constraints for an electro‐mechanical linear actuator (EMLA) facing model uncertainty and external disturbances. At first, a state‐space model of a complex multi‐stage gearbox EMLA system, driven by a permanent magnet synchronous motor (PMSM), is developed, and the non‐ideal characteristics of the ball screw are presented through the model. The result is a four‐order nonlinear strict‐feedback form (NSFF) system decomposed into three subsystems. As the paper's main result, a novel RDSC strategy with uniform exponential stability for controlling subsystem states is presented. This developed controller avoids the "explosion of complexity" problem associated with backstepping by treating the time derivative of the virtual control input as an uncertain system term. The proposed method, despite assuming load disturbances and input constraints with arbitrary bounds, offers a straightforward control approach for a broader range of applications. Further, the controller's performance is evaluated by simulating two distinct duty cycles, each representing different levels of demand on the actuator facing load disturbances near the rated motor performance.

show abstract

“…The traditional PI control strategy has the problems of poor system stability and low control precision, which can't meet the requirements of PMSM high-performance control. At present, the research on PMSM control strategy mainly includes: neural network method [1,2,3,4]; Model reference adaptive method [5,6,7]; State observer [8,9,11]; High frequency injection signal method [12,13,14,15,16], etc [17,18,19]. However, the traditional MRAS has some problems, such as large parameter perturbation, slow reaction speed and poor tracking accuracy, which have triggered a large number of scholars at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

Research on sensorless control of PMSM based on novel double-sliding mode MRAS

Wang,

Li,

et al. 2024

IEICE Electron. Express

View full text Add to dashboard Cite

In order to solve the problems of the traditional model reference adaptive system (MRAS) speed estimation adaptive mechanism, such as poor tracking accuracy and slow response, A new dual sliding mode adaptive observer is designed to estimate the rotational speed and rotor position of permanent magnet synchronous motor (PMSM). Firstly, a new two-variable complex reaching law is used to replace the traditional firstorder sliding mode for velocity loop control, and an anti-saturation scheme is introduced to improve the anti-interference ability of the system. Secondly, the PI adaptive mechanism in the traditional MRAS is replaced by a new super-twisting weighted integral type algorithm adaptive observer (STWITA-AO)， which effectively speeds up the convergence of the estimated speed and reduces the inherent chattering of the sliding mode structure. Finally, the relevant simulation is compared and verified. The simulation results show that the estimation strategy based on the new double sliding mode MRAS control can make the speed estimate converge to the actual value faster, and improve the dynamic performance and robustness of the observer.

show abstract

Command Filtering-Based Neural Network Control for Fractional-Order PMSM With Input Saturation

Cited by 10 publications

References 40 publications

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

Robust decomposed system control for an electro‐mechanical linear actuator mechanism under input constraints

Research on sensorless control of PMSM based on novel double-sliding mode MRAS

Contact Info

Product

Resources

About