This monograph explores the analysis and design of model-free
optimal control systems based on reinforcement learning (RL)
theory, presenting new methods that overcome recent challenges
faced by RL. New developments in the design of sensor data
efficient RL algorithms are demonstrated that not only reduce the
requirement of sensors by means of output feedback, but also ensure
optimality and stability guarantees. A variety of practical
challenges are considered, including disturbance rejection, control
constraints, and communication delays. Ideas from game theory are
incorporated to solve output feedback disturbance rejection
problems, and the concepts of low gain feedback control are
employed to develop RL controllers that achieve global stability
under control constraints. Output Feedback Reinforcement Learning
Control for Linear Systems will be a valuable reference for
graduate students, control theorists working on optimal control
systems, engineers, and applied mathematicians.
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