In this chapter, we propose a framework of robust adaptive dynamic programming (for short, robust-ADP), which is aimed at computing globally asymptotically stabilizing control laws with robustness to dynamic uncertainties, via off-line/on-line learning. It is shown that robust optimal control problems can be solved for higherdimensional, partially linear composite systems by integration of ADP and modern nonlinear control design tools such as backstepping and ISS small-gain methods. Finally, the robust-ADP framework is applied to the load-frequency control for a power system and the controller design for a machine tool power drive system.
- Asymptotic, stabilizing control laws/uncertainties
- Optimality, robust-ADP for partial-state
- Robust ADP, robust-ADP
- Robust-ADP for disturbance attenuation
- Robust-ADP, via off-line/on-line learning
ASJC Scopus subject areas