虚拟控制系数未知的非完整系统的自适应神经网络控制

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.     

Adaptive output feedback control for uncertain nonholonomic chained systems

An adaptive output feedback control was proposed to deal with a class of nonholonomic systems in chained form with strong nonlinear disturbances and drift terms. The objective was to design adaptive nonlinear output feedback laws such that the closed-loop systems were globally asymptotically stable, while the estimated parameters remained bounded. The proposed systematic strategy combined input-state-scaling with backstepping technique. The adaptive output feedback controller was designed for a general case of uncertain chained system. Furthermore, one special case was considered. Simulation results demonstrate the effectiveness of the proposed controllers.     

基于Backstepping的轮式移动机器人镇定研究与实验验证

根据轮式移动机器人的运动学模型,基于Back-stepping方法,构造了轮式移动机器人镇定系统的李雅普诺夫函数,并通过使李雅普诺夫函数负定,设计了移动机器人镇定控制律.轮式移动机器人的仿真实验以及实际的控制实验表明,该方法能使轮式移动机器人的伍姿从任意θ(0)≠0的初始状态渐近收敛到原点.