Automatic Control Department, Qingdao University of Science and Technology, 266061, China.
ISA Trans. 2021 Mar;109:81-88. doi: 10.1016/j.isatra.2020.10.019. Epub 2020 Oct 6.
In the present paper, an active disturbance rejection control(ADRC) scheme via radial basis function(RBF) neural networks is designed for adaptive control of non-affine nonlinear systems facing hysteresis disturbance in which RBF neural network approximation is utilized to tackle the system uncertainties and ADRC is designed to real-time estimate and compensate disturbance with unknown backlash-like hysteresis. Combining the adaptive neural networks design with ADRC design techniques, a new dual-channel composite controller scheme is developed herein whereby adaptive neural networks are used as feed-forward inverse control and ADRC as closed-loop feedback control. Furthermore, as compared to adaptive neural networks control algorithm, the proposed RBF-ADRC dual-channel composite controller can guarantee that the desired signal can be tracked with a small domain of the origin and it is confirmed to be effective under Lyapunov stability theory and MATLAB simulations.
在本文中，设计了一种基于径向基函数（RBF）神经网络的主动干扰抑制控制（ADRC）方案，用于自适应控制具有滞后干扰的非仿射非线性系统，其中利用 RBF 神经网络逼近来处理系统不确定性，ADRC 用于实时估计和补偿具有未知滞后似的干扰。将自适应神经网络设计与 ADRC 设计技术相结合，本文提出了一种新的双通道复合控制器方案，其中自适应神经网络用作前馈逆控制，ADRC 用作闭环反馈控制。此外，与自适应神经网络控制算法相比，所提出的 RBF-ADRC 双通道复合控制器可以保证在原点的小域内跟踪期望信号，并在 Lyapunov 稳定性理论和 MATLAB 仿真中得到验证是有效的。
