基于动态面控制的MIMO自适应神经网络控制

MIMO adaptive neural network control using dynamic surface control

针对一类具有未知函数控制增益的多输入多输出(M IM O)非线性系统,基于后推设计方法和动态面控制技术,提出一种间接自适应神经网络控制方案.该方案通过引入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,并避免了反馈线性化方法可能出现的控制器奇异性问题,参数估计无需使用投影算法.利用李亚普诺夫方法,证明了闭环系统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.仿真结果表明了该方法的有效性.

Based on backstepping and dynamic surface control, a novel design scheme of adaptive neural network controller for a class of MIMO nonlinear systems with unknown function control gain is proposed 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 system is shown to be semi-globally uniformly ultimately bounded, with tracking errors converging to a small neighborhood of origin by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the approach.