This article presents the design of an adaptive backstepping robust optimal control (ABROC) approach for achieving performance with high dynamic of high-speed micro permanent-magnet synchronous motor (HS-MPMSM) drive. First, a backstepping controller is designed for stabilizing the HS-MPMSM drive. To enhance the performance of the control system against external disturbances and parameter variations, an adaptive backstepping robust controller (ABRC) is developed. The ABRC combines a backstepping controller, an adaptive self-constructing fuzzy wavelet neural network (SCFWNN) identifier, and a robust controller. The proposed identifier is developed to approximate the nonlinear functions online. Furthermore, the robust controller is designed to recover the SCFWNN approximation errors. As the online adaptive control laws are derived via Lyapunov theory, thus, the ABRC stability is assured. To attain the optimal control performance, an infinite horizon optimal controller using a critic neural-network (NN) is developed and combined with ABRC to construct the ABROC approach. The critic NN is developed to approximate the optimal value function of the Hamilton–Jacobi–Bellman equation, which is used to develop the optimal controller. The experimental results are presented to verify the effectiveness of the proposed approach. The results validate that the ABROC approach is robust against parameter uncertainties and external disturbances.

中文翻译：

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自建模糊小波神经网络对高速永磁同步电机驱动器的鲁棒优化控制

本文介绍了一种自适应反步鲁棒优化控制 (ABROC) 方法的设计，以实现高速微型永磁同步电机 (HS-MPMSM) 驱动器的高动态性能。首先，反步控制器设计用于稳定 HS-MPMSM 驱动器。为了提高控制系统对外部干扰和参数变化的性能，开发了一种自适应反步鲁棒控制器（ABRC）。ABRC 结合了反步控制器、自适应自构建模糊小波神经网络 (SCFWNN) 标识符和鲁棒控制器。所提出的标识符被开发用于在线逼近非线性函数。此外，鲁棒控制器旨在恢复 SCFWNN 近似误差。由于在线自适应控制律是通过李雅普诺夫理论推导出来的，从而保证了 ABRC 的稳定性。为了获得最佳控制性能，开发了一个使用批评神经网络 (NN) 的无限范围最优控制器，并与 ABRC 相结合以构建 ABROC 方法。开发评论家神经网络以逼近 Hamilton-Jacobi-Bellman 方程的最优值函数，该方程用于开发最优控制器。给出了实验结果以验证所提出方法的有效性。结果验证了 ABROC 方法对参数不确定性和外部干扰的鲁棒性。开发了一个使用批评神经网络 (NN) 的无限视野最优控制器，并与 ABRC 相结合以构建 ABROC 方法。开发评论家神经网络以逼近 Hamilton-Jacobi-Bellman 方程的最优值函数，该方程用于开发最优控制器。给出了实验结果以验证所提出方法的有效性。结果验证了 ABROC 方法对参数不确定性和外部干扰的鲁棒性。开发了一个使用批评神经网络 (NN) 的无限视野最优控制器，并与 ABRC 相结合以构建 ABROC 方法。开发评论家神经网络以逼近 Hamilton-Jacobi-Bellman 方程的最优值函数，该方程用于开发最优控制器。给出了实验结果以验证所提出方法的有效性。结果验证了 ABROC 方法对参数不确定性和外部干扰的鲁棒性。