Robust emerged artificial intelligence speed controller for PMSM drive

Rayd, Hamza; Idrissi, Aziz El Janati El; Zahid, Noureddine; Jedra, Mohamed

doi:10.1109/icocs.2014.7060952

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The two gains K p = 4.2105 and K I = 376.7313, of PI controller are given according to the expressions of Rayd et al (2014). In order to better show the effectiveness of the proposed method, the parameters of the two backlash actuators are considered with the parameters m d = 0.5, m q = 0.55, d d+ = 0.2, d d− = 0.2, d q+ = 0.2 and d q− = 0.2.…”

Section: Simulation Resultsmentioning

confidence: 99%

Stable Control Scheme for Electric Vehicles Based on Uncertainty Compensation with Adaptive Neural Networks

Idrissi

Beniysa

Britel

et al. 2021

J Control Autom Electr Syst

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With the rising problem of global pollution and the decrease in world petroleum resources, electric vehicles are becoming a necessity. This paper proposes a stable control scheme of electric vehicles based on permanent magnet synchronous motor traction, where the trend is to compensate internal and external uncertainties. To compensate these uncertainties, the control of direct and quadrature current loops is adjusted by two novel adaptive compensators by using discrete-time artificial neural network-based online tuning. To prove the system stability, the Lyapunov function is applied. To test the robustness of the proposed scheme, the system dynamic results are simulated using the visual studio C++ software and plotted with Matlab. The simulation results show the effectiveness of the proposed control scheme compared to classical PI vector control.

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Section: Simulation Resultsmentioning

confidence: 99%

Stable Control Scheme for Electric Vehicles Based on Uncertainty Compensation with Adaptive Neural Networks

Idrissi

Beniysa

Britel

et al. 2021

J Control Autom Electr Syst

View full text Add to dashboard Cite

show abstract

“…To handle the speed trajectory, a PI controller is considered. The expressions of its gains K p and K I are determined by the following equations, respectively (Rayd et al, 2014):…”

Section: Discrete-time Pi Controller For the Speedmentioning

confidence: 99%

“…However, these control strategies require an accurate mathematical model of the dynamical system. To achieve this, other modern optimized and intelligent techniques are proposed in the literature (Alizadeh and Kojori, 2015;Beddar et al, 2016;Hong et al, 2013;Jan et al, 2008;Lin et al, 2011;Mousa et al, 2019;Rayd et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

et al. 2021

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This paper proposes a design scheme along with stability analysis of a new adaptive backstepping controller designed for permanent magnet synchronous generator-based wind turbine, by using artificial neural network-based uncertainty compensation. The idea is to control the rotor speed and the mechanical power generated under internal and external nonlinear parametric uncertainties. An uncertain model of permanent magnet synchronous generator is designed. Then, two artificial neural network compensators are built to compensate such uncertainties in the current loops. The stability of the closed-loop system is studied according to the Lyapunov function. Simulations of the dynamic model are performed under both variable step and random wind speeds by using the DEV-C++ software, and the results are plotted with MATLAB. Compared to the classical direct torque control technique, the obtained results show the robustness of the proposed controller despite the presence of different uncertainties.

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“…However, these mentioned control methods require the exact mathematical model of the dynamical system, but the model is approximated by ignoring some physical phenomena. To overcome this problem others modern optimized and intelligent techniques are proposed in the literatures [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The results indicate that the proposed approach is successful and simple to control PMSM systems in spite of the uncertainty problems. physical laws, the three-phase mathematical model of PMSM can be expressed easily in three-phase (abc) frame [5][6][7][8][9][10][11][12][13][14][15][16][17]. Then, by developing this coupled three-phase mathematical model of PMSM, the (dq) axis of current, voltage and flux can be also obtained from Park and Concordia transformations [33].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators

Idrissi¹,

Beniysa²,

Bouajaj³

et al. 2021

JESA

Self Cite

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In this paper, stable and adaptive neural network compensators are proposed to control the uncertain permanent magnet synchronous motor (PMSM). Firstly, the overall uncertainties caused by mathematical modelling, parameters variation during operation and external load torque disturbances are modelled. Secondly, a new motion control scheme, where (d-q) current loops are dotted by two on-line tuning neural network compensators (NNCs), is used to compensate these uncertainties. As a result, the speed control loop is processed easily by proportional integral (PI) controller. Stability of the closed-loop system is also designed according to the Lyapunov stability. Compared to classical vector control, the simulations of PMSM system at different speeds including nominal, low and high speed, with and without uncertainties, show the effectiveness of the proposed control scheme.

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Robust emerged artificial intelligence speed controller for PMSM drive

Cited by 5 publications

References 16 publications

Stable Control Scheme for Electric Vehicles Based on Uncertainty Compensation with Adaptive Neural Networks

Stable Control Scheme for Electric Vehicles Based on Uncertainty Compensation with Adaptive Neural Networks

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

Intelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators

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