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.

在本文中，针对在输入饱和存在下具有全状态非对称时变约束的混沌永磁同​​步电机 (PMSM) 设计了一种有效的自适应控制。这项工作中建议的策略配备了命令过滤，用于解决常见反步方法中可用的“复杂性爆炸”问题。此外，过滤误差被纳入控制设计程序，以提高控制系统的性能。在控制设计过程中，采用了非对称势垒李雅普诺夫函数 (BLF)，以保证给定区间内的状态变量的限制。在建议的控制方法中，为了逼近未知的非线性函数，Bessel 级数被用作一种简单但有效的函数逼近方法作为通用逼近器。所提出的设计提供了这一优势，即通过考虑实际考虑，还设计了降阶观测器，因此无需安装物理传感器来测量混沌 PMSM 的位置和速度。Lyapunov 稳定性理论用于建立所有闭环信号的有界性。根据与神经网络获得的比较结果，所提出的控制设计能够抑制 PMSM 驱动系统的混沌行为，同时确保出色的跟踪性能。还设计了降阶观测器，因此无需安装物理传感器来测量混沌 PMSM 的位置和速度。Lyapunov 稳定性理论用于建立所有闭环信号的有界性。根据与神经网络获得的比较结果，所提出的控制设计能够抑制 PMSM 驱动系统的混沌行为，同时确保出色的跟踪性能。还设计了降阶观测器，因此无需安装物理传感器来测量混沌 PMSM 的位置和速度。Lyapunov 稳定性理论用于建立所有闭环信号的有界性。根据与神经网络获得的比较结果，所提出的控制设计能够抑制 PMSM 驱动系统的混沌行为，同时确保出色的跟踪性能。