In this study, an improved Adaptive Fast Terminal Synergetic Controller (AFTSC) using Dual Radial Basis Function (RBF) Neural Networks (NNs) for output voltage control of an uncertain DC/DC step-down converters is proposed. Using the considered AFTSC, the with new manifold proposed here enables the DC/DC step-down converter’s state variables to track the preferred reference voltage in presence of disturbances from any initial condition with proper precision and limited time. To rendering the design more robust, a sort of dual RBFNNs are utilized to approximate in real-time unknown converter non-linear dynamics and reduce the modeling error without calling upon usual model linearization and simplifications. The stability of the closed-loop system is assured by means of the Lyapunov method. Considering the PWM DC–DC step-down converter as an example, the considered adaptive RBFNN-FTSC law is studied in detail and implemented on a dSPACE ds1103 card. All the simulation and experimental results illustrate the efficiency and feasibility of the suggested controller.

在这项研究中，提出了一种改进的自适应快速终端协同控制器 (AFTSC)，它使用双径向基函数 (RBF) 神经网络 (NN) 来控制不确定的 DC/DC 降压转换器的输出电压。使用所考虑的 AFTSC，这里提出的新流形使 DC/DC 降压转换器的状态变量能够在存在来自任何初始条件的干扰的情况下以适当的精度和有限的时间跟踪首选参考电压。为了使设计更加稳健，使用一种双 RBFNN 来实时逼近未知转换器的非线性动态并减少建模误差，而无需调用通常的模型线性化和简化。闭环系统的稳定性是通过李雅普诺夫方法来保证的。以 PWM DC-DC 降压转换器为例，详细研究了所考虑的自适应 RBFNN-FTSC 定律并在 dSPACE ds1103 卡上实现。所有的仿真和实验结果都说明了所建议控制器的效率和可行性。