We present a control framework for achieving a robust object grasp and manipulation in hand. In-hand manipulation remains a demanding task as the object is never stable and task success relies on carefully synchronizing the fingers' dynamics. Indeed, fingers must simultaneously generate motion while maintaining contact with the object and, by staying within the hand's frame, ensuring that the object remains manipulable. These challenges are exacerbated once the hand gets disturbed or when the internal dynamics of the manipulated object are unknown, such as when it is filled with liquid moving during manipulation. We present a control strategy based on coupled dynamical systems (DSs), whereby the fingers move in synchronization using an intermediate dynamics responsible for coordinating fingers. To adapt to changes in forces due to model uncertainties and unexpected disturbances, we employ an adaptive torque-controller combined with a joint impedance regulator that guarantees high tracking accuracy while adapting to dynamic changes. We validate the approach in multiple experiments on 16-degrees-of-freedom robotic hand grasping and manipulating objects with different mass properties, e.g., uneven or varying mass distribution in a glass half-filled with water. We show that the robot can compensate for disturbances generated by internal dynamics and external perturbations. Additionally, we showcase how our controller, in conjunction with learning from human demonstration, provides a robust solution for more complicated manipulations such as finger gaiting.