The Local-Symmetrical-Double-Integral-Type Iterative Learning Control for Dynamics of Industrial Processes with Time Delay in Steady-State Optimization

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.