论文部分内容阅读
基于工业过程稳态优化中递阶控制结构和线性工业过程控制系统中的迭代学习控制规律 ,本文对饱和非线性工业过程控制系统和变增益非线性工业过程控制系统施行迭代学习控制 ,分别给出加权PD 型闭环迭代学习控制算法和加权幂型开闭环迭代学习控制算法 ,提出了期望目标轨线的 δ 可达性和迭代学习算法的ε 收敛性的概念 .利用Bellman Gronwall不等式和λ 范数理论 ,论证了算法的收敛性 .数字仿真表明 ,迭代学习控制能有效改善非线性工业控制系统在稳态优化时的动态品质
Based on the iterative learning control law in the steady-state optimization of industrial process and the linear industrial process control system, Iterative learning control is applied to the saturated nonlinear industrial process control system and variable-gain non-linear industrial process control system, respectively. Weighted PD-type closed-loop iterative learning control algorithm and weighted power-type open-closed-loop iterative learning control algorithm, the concept of δ-reachability of the expected target trajectory and the ε-convergence of the iterative learning algorithm are proposed.With Bellman Gronwall inequality and λ-norm theory , The convergence of the algorithm is demonstrated.The numerical simulation shows that iterative learning control can effectively improve the dynamic quality of nonlinear industrial control system in steady state optimization