In cyber-physical systems, certain tasks are activated according to a rotation source. For example, angular tasks in engine control units are triggered whenever the engine crankshaft reaches a specific angular position. To reduce the workload at high speeds, these tasks also adopt different implementations within different rotation speed intervals. However, current studies are limited to the case that the switching speeds at which task implementations should change are configured at design time. In this article, we propose to dynamically adjust the switching speeds at runtime. We develop schedulability analysis techniques for such systems, including a new digraph-based task model to safely approximate the workload from software tasks triggered at predefined rotation angles. We prove that such task transformation has bounded pessimism. We present exact algorithms to find a finite number of representatives to avoid enumerating (an infinite number of) all job sequences. Experiments on synthetic task systems demonstrate that the proposed approach provides substantial benefits on system schedulability.