滞后工业过程稳态优化进程中的局部对称双积分型迭代学习控制

摘要

The weighted leading local-symmetrical-double-integral-type iterative learning control algorithm is studied for dynamics of industrial processes with time delay in steady-state optimizing. Based on the desired trajectory and real output dynamical information of the control system, a basic iterative learning control algorithm is suggested, its ε-convergence is derived, and the interval length of local symmetrical double integral is determined. Digital simulations show that the iterative learning control algorithm can effectively eliminate the influence of the noise on output signal and can remarkably improve the dynamical performance of the control systems in steady-state optimizing, such as to decrease the overshoot, shorten the settling time, accelerate the response, etc.%对具有滞后工业过程稳态优化进程提出加权超前局部对称双积分型迭代学习控制算法.基于理想轨线与控制系统的实际输出动态信息,提出基本的迭代学习控制算法并分析和论证算法的收敛性,给出局部对称积分区间参数的确定策略.数字仿真表明,加权超前局部对称双积分型迭代学习控制算法能有效消除噪声对系统输出信号的影响并能改善滞后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,缩短过渡时间,加快响应速度等.