Complex systems, such as autonomous vehicles and missile guidance systems, use hierarchical control schemes where each control layer employs a different system model. This approach enhances computational efficiency because using a simpler model in the higher-level control layer reduces computation times, enabling real-time control strategies. This dissertation presents a framework in which a lower-fidelity planning model is employed for online planning, and a tracking controller, synthesized offline, keeps the tracking error between the high-fidelity (“tracking”) model and the planning model within a bounded set. To ensure safety, the error that arises from the different models in each control layer is rigorously accounted for through augmentation of the planner safety constraints with the tracking error bound. Accommodating more sources of real-world uncertainty enhances the safety and usefulness of the control scheme. We next describe a robust extension which utilizes integral quadratic constraints to accommodate input uncertainties such as unknown delays or unmodeled actuator dynamics in the tracking model. Finally, through a case study of shared vehicle control between a human driver and a supervisory autonomous system in longitudinal driving scenarios, we present a novel method called Driver-in-the-Loop Contingency MPC that leverages simplified dynamics to compute invariant sets that guarantee safety with respect to other vehicles. This contribution can be viewed as adding robustness to other agents in the planning layer.