  We introduce and analyze a model for the dynamics of flocking and steering of
a finite number of agents. In this model, each agent's acceleration consists of
flocking and steering components. The flocking component is a generalization of
many of the existing models and allows for the incorporation of many real world
features such as acceleration bounds, partial masking effects and orientation
bias. The steering component is also integral to capture real world phenomena.
We provide rigorous sufficient conditions under which the agents flock and
steer together. We also provide a formal singular perturbation study of the
situation where flocking happens much faster than steering. We end our work by
providing some numerical simulations to illustrate our theoretical results.
