针对具有未知动力学的机械臂系统 ,提出一系列神经模糊自适应控制方法。提出神经模糊动态逆稳定自适应控制方法 ,该方法使用动态神经模糊系统逼近非线性动态系统 ,设计的动态逆控制器可以通过参数的设定保证闭环系统在初始控制段的动态性能 ,而无需事先要求机械臂状态位于某一紧集的假设。结合延时神经模糊网络 ,引入降维观测器估计输出重定义后机械手的速度矢量 ,从而建立了非线性系统的控制器观测器设计的新方法。采用了动态逆和“Back-stepping(后退 )”的技术 ,将以上方法成功推广到了考虑执行电机动力学特性的柔性连杆机械臂问题上 Aiming at the manipulator system with unknown dynamics, a series of neural fuzzy adaptive control methods are proposed. A dynamic fuzzy inversed adaptive control method is proposed. The method uses a dynamic neural fuzzy system to approximate a nonlinear dynamic system. The designed dynamic inverse controller can ensure the dynamic performance of the closed-loop system in the initial control section through parameter setting without prior The arm state is required to be on a tight assumption. Combined with time-delay neural fuzzy network, a dimensionality observer is introduced to estimate the velocity vector of the manipulator after the redefinition of output. Thus, a new method of designing observer for nonlinear system is established. Using the technique of dynamic inversion and “Back-stepping”, the above method is successfully applied to the problem of flexible link manipulator considering the characteristics of motor dynamics