Employing the singular value decomposition (SVD), quadratic criterion-based iterative learning control (Q-ILC) is analyzed and useful design guidelines for tuning factors are obtained. A reduced-order design technique, which not only reduces computational demand and input energy but also enhances robustness, is also proposed. An example highlights the key features of the proposed reduced-order Q-ILC algorithm.