具有死区输入的非参数不确定系统误差跟踪迭代学习控制

Error tracking iterative learning control for non-parametric uncertain systems with input dead-zone

本文针对一类具有非对称死区输入的非参数不确定系统设计了一种误差跟踪迭代学习控制(ILC)算法。首先,构造一种新型的期望误差轨迹放宽经典迭代学习控制的初值一致条件。其次,利用微分中值定理将非对称死区转换为线性形式,并利用径向基函数(RBF)神经网络对系统不确定性和死区参数进行估计和补偿。在此基础上,设计误差跟踪迭代学习控制器和组合自适应律,实现系统在指定区间对期望轨迹的高精度跟踪。最后,基于Lyapunov-Like理论进行稳定性分析,并通过仿真验证了本文所提方法的有效性。

An error tracking iterative learning control(ILC) is proposed for non-parametric uncertain systems with nonsymmetric dead-zone input. Firstly, a new type of desired error trajectory is constructed to relax the identical initial condition requirement in the classical iterative learning control. Then, the asymmetric dead zone is converted into a linear form by mean-value theorem, and a radial basis function(RBF) neural network is used to estimate the system uncertainty and dead zone parameters. Based on this, an error tracking iterative learning controller and a unified adaptive learning law are designed to achieve high-precision tracking of the desired trajectory in the specified interval. Finally, the stability analysis is demonstrated through the Lyapunov-Like method, and numerical simulation results are provided to demonstrate the effectiveness of the proposed scheme.