Predictor-Based Adaptive Plant Augmentation Design With Application to Hierarchical Control

This letter presents a predictor-based adaptive augmentation scheme to recover the designed behavior of a baseline linear controller in presence of parametric uncertainty. Remarkably, the proposed scheme achieves the recovery of the baseline closed-loop performance without the need for explicit knowledge of the baseline controller states and structure; rather, the adaptive mechanism relies solely on the output of the baseline controller and plant states. We showcase how the proposed adaptive design seamlessly integrates into inner-outer loop control architectures, enhancing the overall performance and robustness while simultaneously reducing the control law complexity compared to available solutions.