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OA

For a class of systems with uncertainties and whose performance is highly dependent on histori-cal data,traditional first-order iterative learning control(ILC)struggles to effectively utilize historical informa-tion,facing limitations such as insufficient convergence accuracy and difficulties in ensuring stability.To ad-dress 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.Fi-nally,the effectiveness of the proposed method is verified through numerical simulations.