混沌反控制是指通过对在自然状态下并不存在混沌的系统设计控制方案来实现闭环系统的混沌。本文针对带有未知参数和有界扰动的机械臂，设计了一种带有自适应终端滑模项的RBF神经网络控制器来实现机械臂的混沌反控制。采用RBF神经网络逼近系统的未知非线性函数，通过自适应终端滑模实现系统对外界扰动的鲁棒性。利用Lyapunov稳定性定理证明了闭环系统所有信号的最终一致有界。最后，以两连杆平面机械臂为例，通过仿真验证所提方法的可行性与有效性。

Anti-control of chaos consists in injecting a chaotic behavior by means of a control scheme to a system, which in natural form does not present it. In this paper, an RBF neural network control algorithm with adaptive terminal sliding mode is proposed to realize the anti - control of chaos for manipulator with unknown parameters and bounded disturbances. The RBF neural network is used to approximate the unknown nonlinear function of the system and the adaptive terminal sliding mode control is used to achieve robustness of the system to external disturbances. The uniform ultimate boundedness of all the signals in the closed-loop system is proved by the Lyapunov stability theorem. Finally, take a two-link planar robot manipulator as a simulation example to verify the feasibility and effectiveness of the proposed method.