基于高阶采样数据迭代学习的非线性不确定系统跟踪控制-《集美大学学报（自然科学版）》

文章信息/Info

Title:: Tracking Control of Nonlinear Uncertain Systems Based on Iterative Learning with High-Order Sampled Data

Author(s):: SHEN Yanting; HUANG Zhenkun; (School of Science，Jimei University，Xiamen 361021，China)

Keywords:: system tracking control; iterstive learning control (ILC); sampled data; P-type learning; convergence analysis

摘要:: 针对一类具有不确定性且性能高度依赖历史数据的系统，传统一阶迭代学习控制（ILC）难以有效利用历史信息，存在收敛精度不足、难以保障稳定性等局限。为解决上述问题，本文提出了一种高阶采样数据ILC策略。该策略通过设计采样数据机制，显著降低了数据存储与通信需求；通过引入高阶学习律，并融合多个历史批次中的有效数据，增强了系统对不确定性扰动的抑制能力。基于线性递推不等式收敛引理及Bellman-Gronwall引理，建立了系统渐近收敛的充分条件。最后，通过数值模拟验证了所提方法的有效性。

Abstract:: For a class of systems with uncertainties and whose performance is highly dependent on historical data,traditional first-order iterative learning control (ILC) struggles to effectively utilize historical information,facing limitations such as insufficient convergence accuracy and difficulties in ensuring stability.To address these issues,this paper proposes a high-order sampled data ILC strategy.By designing a sampled data mechanism,the data storage and communication requirements are significantly reduced.By introducing a high-order learning law and integrating effective data from multiple historical batches,the system’s ability to suppress uncertain disturbances is enhanced.Based on the convergence lemma of linear recursive inequalities and the Bellman-Gronwall lemma,sufficient conditions for the asymptotic convergence of the system are established.Finally,the effectiveness of the proposed method is verified through numerical simulations.