具有死区的饱和工业过程控制系统的迭代学习控制

Iterative Learning Control for Saturated Industrial Proces s Control Systems with Dead Zone

对具有死区的非光滑饱和工业过程的稳态优化进程施加迭代学习控制 ,给出加权开环PD型迭代学习控制算法 .算法基于前次迭代的输出动态信息和事先给定的理想轨线 ,修正工业过程控制系统的阶跃输入 ,以期改善控制系统的动态品质 .给出了理想轨线的选取方法 ,提出了理想轨线的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ε收敛性 .数字仿真表明 ,迭代学习控制能有效改善工业过程稳态优化进程中控制系统的动态品质 ,如减少超调 ,加快动态响应速度 ,缩短过渡时间等 ,显示了算法对工业过程控制系统的有效性 .

The iterative learning control is firstly studied for non-smooth saturated industrial process control systems with dead zone. The weighted open-loop DP-type algorithm is suggested which modifies the step input of the control systems based on the dynamic output of previous iteration and the given desired trajectory in order to improve the dynamic performance. The choice of desired trajectory is given, the delta-reachability of the desired trajectory and the epsilon-convergence of the algorithm are suggested. By means of Bellman-Gronwall inequality and lambda-norm theory, the epsilon-convergence of the algorithm with respect to the system is proved. Digital simulations show that the iterative learning control can remarkably improve the dynamic performance of the control systems, such as decrease the overshoot, speed up the dynamic response and shorten the settling time, etc. This indicates the effectiveness of the algorithm on the industrial control system.