基于动态面控制的间接自适应神经网络块控制

针对一类可转化为"标准块控制形"的多输入多输出的非线性系统,基于动态面控制技术,提出一种间接自适应神经网络控制器的设计方案.该方法通过引入1阶滤波器,消除了后推设计中由于反复对虚拟控制的求导而导致的复杂性问题,同时完全避免了反馈线性化方法中可能出现的控制器奇异性问题,且无需控制增益矩阵正定、可逆的条件.利用李亚普诺夫方法,证明了闭环系统是半全局一致终结有界,通过适当选取设计常数,跟踪误差可收敛到原点的一个小邻域内.仿真结果表明所提控制方法的有效性.

Indirect adaptive neural network block control using dynamic surface control

Based on dynamic surface control,a novel design scheme of adaptive neural network controller is proposed for a class of MIMO nonlinear systems which could be turned to "standard block control type",without inverse gain matrix in this paper.The problem of explosion of complexity in traditional backstepping design,which is caused by repeated differentiations of certain nonlinear functions such as virtual control,is overcome by introducing the first order filter.Moreover,the possible controller singularity in feedback linearization is avoided without projection algorithm.Using Lyapunov method,the closed-loop systems is shown to be semi-globally uniformly ultimately bounded,with tracking error converging to a small neighborhood of origin by appropriately choosing design constants.Simulation results demonstrate the effectiveness of the proposed method.