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In this chapter, we present a framework for online control customization that makes use of finite-horizon, optimal control combined with real-time trajectory generation and optimization. The results are based on a novel formulation of receding-horizon optimal control that replaces the traditional terminal constraints with a control Lyapunov function-based terminal cost. This formulation leads to reduced computational requirements and allows proof of stability under a variety of realistic assumptions on computation. By combining these theoretical advances with advances in computational power for real-time, embedded systems, we demonstrate the efficacy of online control customization via optimization-based control. The results are demonstrated using a nonlinear, flight control experiment at Caltech.