【摘要】 针对一类未建模动态和死区非线性输入的非仿射系统,提出一种自适应神经网络控制器设计方案.该方法拓展了未建模动态的变量范围,将后推技术与动态面控制(dynamic surface control,DSC)技术相结合,有效消除了后推法的计算膨胀问题,降低了控制器的复杂性;借助中值定理和Young’s不等式,使后推设计的每一步仅需调节1个参数,从而放宽了动态不确定性的假设,无须虚拟控制增益系数导数的信息.理论分析证明闭环系统所有信号半全局一致终结有界,并且可通过调节设计参数使跟踪误差能够收敛到一个小邻域内.仿真结果表明了该控制方法的有效性.更多还原

【Abstract】 The adaptive neural network controller is proposed for a class of completely non-affine pure-feedback nonlinear with dead-zone nonlinear input and unmodeled dynamics.This scheme broadens the variables of unmodeled dynamics.Combining dynamic surface control(DSC)with backstepping technique,the algorithm can overcome the"explosion of complexity"problem.Using the mean value theorem and Young’s inequality,only one learning parameter needs to be tuned online at each step of recursion.By theoretical analysis,the closed-loop systems are shown to be semi-globally uniformly ultimately bounded,with tracking error converging to a small neighborhood by appropriately choosing design constants.Simulation results demonstrate the effectiveness of the proposed method.更多还原