虚拟控制系数未知的非完整系统的自适应神经网络控制

Adaptive neural control of uncertain nonholonomic systems with unknown virtual control coefficients

针对一类虚拟控制系数未知的多输入链式非完整控制系统,提出了一种自适应神经网络控制策略.在控制策略的设计中,采用了State-scaling与Backstepping技术相结合的方法.Nussbaum-type增益技术用来解决系统的控制方向完全未知的问题.所提出的自适应神经网络控制策略解决了由复杂系统所引起的奇异问题,并通过选择适当的控制参数,使闭环系统半全局一致有界,且系统的状态渐近收敛到包含原点的任意小的一个收敛域.一种基于切换策略的自适应控制方法解决了当x0(t0)=0时所引起的系统不可控问题.仿真结果验证了算法的有效性.

An adaptive neural network control is applied to nonholonomic systems in chain form,with unknown virtual control coefficients and strong drift nonlinearities.The Backstepping technique and state-scaling are employed in design-ing the adaptive neural network control laws.Nussbaum-type functions are used to solve the problem of the completely unknown control direction.The uniform ultimate boundedness of all signals in the closed-loop is guaranteed;and the systems states are proven to converge to a small neighborhood of zero.The control performance of the closed-loop system is achieved by appropriately choosing the design parameters.The proposed adaptive neural network control is free from the control singularity problem.An adaptive control-based switching strategy is used to overcome the uncontrollability problem associated with x0（t0） = 0.Simulation results are provided to show the effectiveness of the proposed approach.