Generalization of ILC for fixed order reference trajectories using interpolation

• Published in: 2022 IEEE 17th International Conference on Advanced Motion Control (AMC) pp. 294 - 299
• Main Authors: Bolderman, Max, Erens, Gerben, Lazar, Mircea, Butler, Hans
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.02.2022
• Subjects: feed-forward control; Iterative learning control; Mechatronics; Noise measurement; Process control; Sensitivity; servo tracking; Tracking; Training; Trajectory
• ISSN: 1943-6580
• DOI: 10.1109/AMC51637.2022.9729275

The increasing demands for motion accuracy in high-precision mechatronics call for intelligent solutions to feedforward controller design. Iterative learning control (ILC) produces data-driven feedforward signals that give high accuracy for repeating references. However, the ILC feedforward input requires time consuming re-learning for each variation of the reference. In order to circumvent the re-learning process, this paper presents a feedforward controller design that can handle fixed order references. First, we assume that ILC is used to obtain feedforward signals for a finite number of repeating references, and that these references can be split into sections that admit a polynomial parameterization. Then, we show that a new feedforward input can be calculated from the existing ILC signals for any polynomial reference spanned by parameter-wise linear combinations of the learned references. Effectiveness of the method is shown in simulation of a coreless linear motor.