To obtain explainable guarantees in the online synthesis of optimal controllers for high-integrity cyber-physical systems, we re-investigate the use of exhaustive search as a classical alternative to reinforcement learning. We model an application scenario as a hybrid game automaton, enabling the synthesis of robustly correct and near-optimal controllers online without prior training. We allow model uncertainty through disturbed dynamics yielding a robust reference signal for lower-level trajectory tracking. For modal synthesis, we employ discretised games solved via scope-adaptive and step-pre-shielded discrete dynamic programming. In a simulation-based experiment, we apply our approach to an autonomous aerial vehicle scenario. We propose a parametric system model and a parametric online synthesis.