Ruan Wei, Dong Quanlin, Zhang Xiaoyue, Li Zhibing
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2021 Feb 22;21(4):1508. doi: 10.3390/s21041508.
In this paper, a radial basis neural network adaptive sliding mode controller (RBF-NN ASMC) for nonlinear electromechanical actuator systems is proposed. The radial basis function neural network (RBF-NN) control algorithm is used to compensate for the friction disturbance torque in the electromechanical actuator system. An adaptive law was used to adjust the weights of the neural network to achieve real-time compensation of friction. The sliding mode controller is designed to suppress the model uncertainty and external disturbance effects of the electromechanical actuator system. The stability of the RBF-NN ASMC is analyzed by Lyapunov's stability theory, and the effectiveness of this method is verified by simulation. The results show that the control strategy not only has a better compensation effect on friction but also has better anti-interference ability, which makes the electromechanical actuator system have better steady-state and dynamic performance.
本文提出了一种用于非线性机电作动器系统的径向基神经网络自适应滑模控制器（RBF-NN ASMC）。采用径向基函数神经网络（RBF-NN）控制算法来补偿机电作动器系统中的摩擦干扰转矩。利用自适应律调整神经网络的权重，以实现对摩擦的实时补偿。设计滑模控制器来抑制机电作动器系统的模型不确定性和外部干扰影响。通过李雅普诺夫稳定性理论分析了RBF-NN ASMC的稳定性，并通过仿真验证了该方法的有效性。结果表明，该控制策略不仅对摩擦具有较好的补偿效果，而且具有较好的抗干扰能力，使机电作动器系统具有更好的稳态和动态性能。
