非线性大系统的跟踪具有不同幅值轨线的分散型迭代学习控制器

在大型工业过程递阶稳态优化中, 可行的方法是利用系统的实际信息以修正基于模型的最优解. 在这种情形下, 得出一幅值不等的阶跃型控制值序列, 而且该控制值序列依次激励实际系统. 本文将一组迭代学习控制器分散地嵌入到一类非线性工业过程的递阶稳态优化进程中, 每一子系统的迭代学习控制器将产生一强化的控制信号序列以替代相应的具有不同幅值的阶跃型控制值序列, 目的是不断改进系统的暂态品质. 通过卷积的 Hausdorff-Young 不等式, 本文分析了学习控制律在 Lebesgue-P 范数意义下的收敛性, 讨论了系统的非线性性和关联性对控制律收敛性的影响. 最后, 数字仿真验证了所研究的学习控制机理的正确性和有效性.

Decentralized Iterative Learning Controllers for Nonlinear Large-scale Systems to Track Trajectories with Different Magnitudes

In hierarchical steady-state optimization programming for large-scale industrial processes,a feasible technique is to use information of the real system so as to modify the model-based optimum.In this circumstance,a sequence of step function-type control decisions with distinct magnitudes is computed,by which the real system is stimulated consecutively.In this paper,a set of iterative learning controllers is embedded into the procedure of hierarchical steady-state optimization in decentralized mode for a class of large-scale nonlinear industrial processes.The controller for each subsystem is used to generate a sequence of upgraded control signals so as to take responsibilities of the sequential step control decisions with distinct scales.The aim of the learning control design is to consecutively refine the transient performance of the system.By means of the Hauedorff-Young inequality of convolution integral,the convergence of the updating rule is analyzed in the sense of Lebesgne-p norm.Invention of the nonlinearity and the interaction on convergence are discussed.Validity and effectiveness of the proposed control scheme are manifested by some simulations.