Direct Data-Driven Control with Embedded Anti-Windup Compensation
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:46-54, 2020.
Input saturation is an ubiquitous nonlinearity in control systems and arises from the fact that all actuators are subject to a maximum power, thereby resulting in a hard limitation on the allowable magnitude of the input effort. In the scientific literature, anti-windup augmentation has been proposed to recover the desired linear closed-loop dynamics during transients, but the effectiveness of such a compensation is strongly linked to the accuracy of the mathematical model of the plant. In this work, it is shown that a feedback controller with embedded anti-windup compensator can be directly identified from data, by suitably extending the existing data-driven design theory. The effectiveness of the resulting method is illustrated on a benchmark simulation example.