PREFACE
This book covers the topic of adaptive optimal control (AOC) for continuous-time
systems. An adaptive optimal controller can gradually modify itself to adapt to the
controlled system, and the adaptation is measured by some performance index of
the closed-loop system. The study of AOC can be traced back to the 1970s, when
researches at the Los Alamos Scientific Laboratory (LASL) started to investigate the
use of adaptive and optimal control techniques in buildings with solar-based temper-
ature control. Compared with conventional adaptive control, AOC has the important
ability to improve energy conservation and system performance. However, even
though there are various ways in AOC to compute the optimal controller, most of the
previously known approaches are model based, in the sense that a model with a fixed
structure is assumed before designing the controller. In addition, these approaches
do not generalize to nonlinear models.
On the other hand, quite a few model-free, data driven approaches for AOC have
emerged in recent years. In particular, adaptive/approximate dynamic programming
(ADP) is a powerful methodology that integrates the idea of reinforcement learning
(RL) observed from mammalian brain with decision theory so that controllers for
man-made systems can learn to achieve optimal performance in spite of uncertainty
about the environment and the lack of detailed system models. Since the 1960s, R-
L has been brought to the computer science and control science literature as a way
to study artificial intelligence, and has been successfully applied to many discrete-
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