高阶参数优化迭代学习控制算法  

High-order parameter-optimization iterative learning control algorithm

作　　者：逄勃[1] 邵诚[1] 

机构地区：[1]大连理工大学先进控制技术研究所,辽宁大连116023

出　　处：《控制理论与应用》2015年第4期561-567,共7页Control Theory & Applications

基　　金：国家自然科学基金项目(61074020)资助~~

摘　　要：针对线性时不变离散系统的跟踪问题提出一种高阶参数优化迭代学习控制算法.该算法通过建立考虑了多次迭代误差影响的参数优化目标函数,求解得出优化后的时变学习增益参数.从理论上证明了:对于线性离散时不变系统,该算法在被控对象不满足正定性的松弛条件下仍可保证跟踪误差单调收敛于零.同时,采用之前多次迭代信息的高阶算法具有更好的收敛性和鲁棒性.最后利用一个仿真实例验证了算法的有效性.A high-order parameter-optimization iterative learning control algorithm is presented for solving the tracking problems of a class of linear time-invariant discrete system. The proposed algorithm is based on a quadratic performance objective function with the tracking errors from earlier trials. By solving this function we obtain the optimal time-varying parameters as the learning gain of the iterative update law. It is proved theoretically that when applied to the relaxed linear discrete system, the proposed algorithm guarantees the tracking error to converge to zero monotonically even the original system is nonpositive. Moreover, since more information of previous iterations is considered in the proposed algorithm, the robustness and convergence performance of the algorithm are improved accordingly. Finally, a case study is carried out to illustrate the performance of this new algorithm.

关 键 词：迭代学习控制 参数优化 单调性 离散系统 线性系统 高阶 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]