Command filtering-based adaptive control for chaotic permanent magnet synchronous motors considering practical considerations

In this paper, an efficient adaptive control is designed for chaotic Permanent Magnet Synchronous Motors (PMSMs) with full-state asymmetric time-varying constraints in the input saturation presence. The strategy that is suggested in this work is equipped with the command filtering for addressing the problem of the explosion of complexity available in the common backstepping method. In addition, the filtering errors are incorporated into the control design procedure for improving the control system performance. During the control design, the asymmetric barrier Lyapunov functions (BLFs) are employed so that the restriction of state variables in the given intervals is guaranteed. In the suggested control method, for approximating unknown nonlinear functions, the Bessel series is utilized as a simple but effective function approximation approach as a universal approximator. The presented design provides this advantage that by considering practical considerations, a reduced-order observer is also designed so that there is no need to mount the physical sensors to measure the position and the velocity of the chaotic PMSM. The Lyapunov stability theory is used to establish the boundedness of all the closed-loop signals. According to the comparative results obtained with neural networks, the presented control design is able to suppress the chaotic behavior of the PMSM drive system while ensuring an excellent tracking performance. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

In this paper, an efficient adaptive control approach is proposed for chaotic PMSMs by using command filtering, Bessel series approximation, and a reduced-order observer to improve control system performance. The asymmetric Lyapunov functions are employed to guarantee the restriction of state variables, and the control design successfully suppresses chaotic behavior while maintaining excellent tracking performance.