In the brain, the collective activity of neurons form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example, different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. Here we propose mechanisms for fast timescale control of the neural subspace in a recurrently connected neural network model for sequence generation. Using mathematical modeling and simulation, we show how a combination of neural mechanisms can dynamically control the speed and path of neural trajectories and flexibly rotate the manifold, forming a basis for fast timescale computation on neural manifolds.