  This paper addresses the problem of finite horizon constrained robust optimal
control for nonlinear systems subject to norm-bounded disturbances. To this
end, the underlying uncertain nonlinear system is decomposed based on a
first-order Taylor series expansion into a nominal system and an error
(deviation) described as an uncertain linear time-varying system. This
decomposition allows us to leverage System Level Synthesis to jointly optimize
an affine error feedback, a nominal nonlinear trajectory, and, most
importantly, a dynamic linearization error over-bound used to ensure robust
constraint satisfaction for the nonlinear system. The proposed approach thereby
results in less conservative planning compared with state-of-the-art
techniques. We demonstrate the benefits of the proposed approach to control the
rotational motion of a rigid body subject to state and input constraints.
