  This paper studies the robust optimal control design for uncertain nonlinear
systems from a perspective of robust adaptive dynamic programming (robust-ADP).
The objective is to fill up a gap in the past literature of ADP where dynamic
uncertainties or unmodeled dynamics are not addressed. A key strategy is to
integrate tools from modern nonlinear control theory, such as the robust
redesign and the backstepping techniques as well as the nonlinear small-gain
theorem, with the theory of ADP. The proposed robust-ADP methodology can be
viewed as a natural extension of ADP to uncertain nonlinear systems. A
practical learning algorithm is developed in this paper, and has been applied
to a sensorimotor control problem.
