非线性非仿射离散时间系统的两阶段最优迭代学习控制

针对非仿射非线性离散时间系统, 基于一种新的沿迭代轴的动态线性化技术, 提出了双层最优迭代学习控制算法. 双层意味着分别设计了两个最优学习层, 迭代的改进控制输入序列和学习增益. 其主要特点是控制器的设计和收敛性分析只依赖于动态系统的 I/O 数据. 换句话说, 不需要知道系统的任何其他信息就可以很容易的选取控制器参数. 仿真研究表明了提出的算法沿迭代轴具有几何收敛性, 这一特点在快速路交通迭代学习控制中具有重要的工程意义.

Dual-stage Optimal Iterative Learning Control for Nonlinear Non-affine Discrete-time Systems

On the basis of a new dynamic linearization technology along the iteration axis, a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems. Dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively. The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system. In other words, we can easily select the control parameters without knowing any other knowledge of the system. Simulation study illustrates the geometrical convergence of the presented method along the iteration axis, in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance.