控制方向未知的二阶时变非线性系统自适应迭代学习控制

摘要

对一类二阶严格反馈时变非线性系统的自适应迭代学习控制问题进行了研究.系统中含有非周期时变参数化不确定性且控制方向未知.首先,提出了一种神经网络估计器,实现了对未知非周期时变非线性函数的逼近.随后,用Nussbaum函数对未知控制方向进行了自适应估计,并综合应用baCkstcpping技术和自适应迭代学习控制技术设计了控制器.所设计的控制器能保证系统所有状态量在Lpe-范数意义下有界,且系统的输出量在LT2-范数意义下收敛到期望轨迹.最后的仿真研究证明了控制器设计方法的有效性.%We investigate the adaptive iterative learning control for a class of second-order strict-feedback nonlinear systems with non-periodically time-varying parameterized uncertainties and unknown control directions. Firstly, a neural network estimator is proposed to approximate unknown non-periodically time-varying nonlinear functions. Subsequently, Nussbaum function is applied to estimate the control directions adaptively. At the same time, the backstepping and adaptive iterative learning control technique are combined to design the controller. The controller guarantees that all state variables are bounded in Lpe-norm and the output tracks the desired trajectory perfectly in LT2-norm. Finally, the effectiveness of proposed scheme is validated by simulation research.