迟滞非线性动态系统神经网络自适应控制

为了减轻非线性动态系统中未知迟滞(Hysteresis)的不良影响，该文提出了一类Backlash型迟滞模型。将有限数量不同宽度的Backlash(Matlab／Simulink)算子进行叠加，来仿真执行器中的迟滞非线性动态。用此模型，提出了基于径向基函数神经网络的自适应控制方案，以控制伴有未知迟滞的非线性动态系统。该方案采用了动态逆的思想及伪控制的概念。利用Lyapunov稳定理论，设计了两个鲁棒控制项，保证动态系统的稳定性、系统中所有信号有界和误差收敛到起点的领域内。通过Matlab／Simulink仿真实验，证明了所提出方案的有效性。

RBFNN-Based Adaptive Control for Hysteresis Nonlinear Dynamic System

In this paper, in order to design control scheme to mitigate the effects of unknown hysteresis, a class of backlash-type hysteresis model is proposed. We superpose a few of different width backlash operators, which representes a dynamics to mimic hysteresis in actuator. Using the model proposed, a RBFNN-based adaptive control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. The control scheme adopts the idea of dynamic inversion and the concept of pseudo-control input. With Lyapuouv theory, through two robustifying control terms, the scheme ensures the dynamic system to be stable, all signals in system to be bounded and tracking error to converge to a neighborhood of zero. The effectiveness of the proposed control scheme is illustrated through simulation.