Output Feedback Reinforcement Learning Control for Linear Systems

Beschreibung

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.
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.
Demonstrates new methods for the design of control systems based on reinforcement learning Presents new new approaches to dealing with disturbance rejections, control constraints, and time delays Incorporates ideas from game theory to solve output feedback disturbance rejection problems

Autor*in

Syed Ali Asad Rizvi

Themen in »Output Feedback Reinforcement Learning Control for Linear Systems«

Reinforcement Learning Reinforcement Learning Algorithms Model-Free Control Model-Free Control Algorithms Optimal Tracking Control Multi-Agent Synchronization Adaptive Dynamic Programming

Stimmen zu »Output Feedback Reinforcement Learning Control for Linear Systems«

Details

Link teilen