In this work, we study a robust dynamic optimization problem for nonlinear fractional impulsive switched systems (FISSs) with uncertain parameters. The main novelty lies in directly incorporating parameter sensitivity into the cost functional to enhance robustness against model uncertainty. To solve the resulting problem, the system sensitivity is first computed through an auxiliary FISS, and a time-scaling transformation is employed to reformulate the optimization over fixed switching instants. Tractable gradient expressions are then derived using a set of auxiliary systems. A gradient-based optimization method is subsequently developed to solve the transformed problem together with a numerical scheme tailored for the FISSs. Two numerical examples demonstrate that the proposed technique achieves effective optimization performance and improved robustness.
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