IEEE Trans Cybern. 2023 Mar;53(3):1392-1404. doi: 10.1109/TCYB.2021.3123614. Epub 2023 Feb 15.
In this study, an adaptive neural network (NN) control using nonlinear information (NI) gain for permanent magnet synchronous motors (PMSMs) is proposed to improve control and estimation performance. The proposed method consists of a nonlinear controller, a three-layer NN approximator, and NI gain. The nonlinear controller is designed via a backstepping procedure for position tracking. The commutation scheme is designed to implement the PMSM control without the direct-quadrature (DQ) transform. The three-layer NN approximator is designed to estimate the unknown complex function generated by the recursive backstepping process. The NI gains are designed to enhance the control and estimation performance according to the increased tracking errors owing to the load torque and the desired position variations. All of signals in the closed-loop system guarantee the semiglobal uniformly ultimately boundness (UUB) using the Lyapunov stability theorem and the input-to-state stability (ISS) property. The performance of the proposed method was validated by experiments performed using a PMSM testbed.
本研究提出了一种基于非线性信息增益（NI）的自适应神经网络（NN）控制方法，用于改善永磁同步电机（PMSM）的控制和估计性能。该方法由非线性控制器、三层 NN 逼近器和 NI 增益组成。非线性控制器通过反推过程设计，用于位置跟踪。换相方案设计用于实现无需直接正交（DQ）变换的 PMSM 控制。三层 NN 逼近器用于估计由递归反推过程产生的未知复杂函数。NI 增益根据负载转矩和期望位置变化引起的跟踪误差的增加来增强控制和估计性能。利用 Lyapunov 稳定性定理和输入-状态稳定性（ISS）特性，闭环系统中的所有信号都保证半全局一致有界（UUB）。通过使用 PMSM 测试平台进行的实验验证了所提出方法的性能。
