سیستمهای کنترلی متکی به الگوریتم ILC ) Iterative Learning Control ) سیستمهای کنترلی جدید و پیشرفته ای هستند که در فرآیندهای تکرار پذیر کاربرد می یابند و بر مبنای تئوری پیشرفته کنترلی Robust شکل گرفته اند. در این نوع سیستمهای کنترلی آموزش پذیر، پارامترهای کنترلی حین عملکرد سیستم می تواند تغییر کند و کاربردهای متعددی در شاخه های مختلف مهندسی از جمله برق دارند. Iterative Learning Control (ILC) techniques have been successfully applied to solve a variety of real-life control-engineering problems, for example mechanical systems such as robotic manipulators, electrical systems such as electrical drives, chemical process systems such as batch reactors, as well as aerodynamic systems, bioengineering systems, and others. When such systems are operated repeatedly, iterative learning control can be used as a novel enabling technology to improve the system response significantly from trial to trial. ILC is reputed for its promising and unique features: the structural simplicity, the perfect output tracking, almost model-independent design, and delay compensation. These highly desirable features make ILC a promising control alternative suitable for numerous real-time control tasks where a simple controller is required to achieve precise tracking in the presence of process uncertainties and delays. Most iterative learning control schemes are designed to find purely feedforward action depending wholly on the previous control performance of an identical task. Although the purely feedforward control scheme is theoretically acceptable, it is difficult to apply to real systems without a feedback control due to several reasons. One of the reasons is that it is not robust against disturbances that are not repeatable with respect to iterations. Another reason is that the tracking error may possibly grow quite large in the early stage of learning, though it eventually converges after a number of trials. In addition, an iterative learning control is designed and analyzed with a mathematical model of the plant. Since modelling errors are unavoidable, the real iterative learning control system may violate its convergence condition although the iterative learning control satisfies the condition for nominal plant model. Thus, in real practice, a feedback control is commonly employed along with the iterative learning control for system robustness enhancement and better performance. In these control schemes, the feedback controller ensures closed-loop stability and suppresses exogenous disturbances and the iterative learning controller provides improved tracking performance over a specific trajectory utilizing past control results.
