基于神经网络的非仿射块控非线性系统动态反演控制

即使已知非仿射非线性系统的逆存在,利用隐函数定理求解该显式逆仍然非常困难.为此,针对一类不确定块控非仿射系统,将动态反馈、反演、神经网络和反馈线性化技术相结合,提出一种自适应鲁棒控制器的设计方法.利用神经网络来逼近和消除未知函数,并证明了整个闭环系统在李雅普诺夫意义下是稳定的.仿真结果表明了所提出方法的有效性.

NN-based dynamic feedback backstepping control for nonaffine block nonlinear systems

The difficulty associated with a control design for nonaffine control systems is that an explicit inverting control design is not possible even if the inverse exists by using the implicit function theorem.Therefore,a robust adaptive control design method is proposed for a class of uncertain block nonaffine systems via incorporating dynamic feedback,backstepping design,neural network,and feedback linearization techniques,in which the NNs are used to approximate and adaptively cancel the unknown functions.The whole closed-loop system is proved to be stable in the sense of Lyapunov.Finally,simulation results show the effectiveness of the proposed scheme.