Open AccessIntelligent Control of Uncertain PMSM Based on Stable and Adaptive Discrete-Time Neural Network Compensators
Author(s) -
Aziz El Janati El Idrissi,
Mohsin Beniysa,
Adel Bouajaj,
Mohammed Réda Britel
Publication year - 2021
Publication title -
journal européen des systèmes automatisés/journal européen des systèmes automaitsés
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.16
H-Index - 20
eISSN - 2116-7087
pISSN - 1269-6935
DOI - 10.18280/jesa.540407
Subject(s) - control theory (sociology) , artificial neural network , controller (irrigation) , lyapunov function , lyapunov stability , stability (learning theory) , adaptive control , torque , engineering , control engineering , vector control , electronic speed control , computer science , control (management) , induction motor , voltage , physics , nonlinear system , artificial intelligence , electrical engineering , quantum mechanics , machine learning , agronomy , biology , thermodynamics
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.