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001453454 001__ 1453454
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001453454 019__ $$a1352415356
001453454 020__ $$a9783031158582$$q(electronic bk.)
001453454 020__ $$a303115858X$$q(electronic bk.)
001453454 020__ $$z3031158571
001453454 020__ $$z9783031158575
001453454 0247_ $$a10.1007/978-3-031-15858-2$$2doi
001453454 035__ $$aSP(OCoLC)1352974896
001453454 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCF
001453454 049__ $$aISEA
001453454 050_4 $$aTJ216
001453454 08204 $$a629.8/3$$223/eng/20221212
001453454 1001_ $$aRizvi, Syed Ali Asad.
001453454 24510 $$aOutput feedback reinforcement learning control for linear systems /$$cSyed Ali Asad Rizvi, Zongli Lin.
001453454 260__ $$aCham :$$bBirkhäuser,$$c2023.
001453454 300__ $$a1 online resource (304 p.).
001453454 4901_ $$aControl Engineering
001453454 500__ $$a3.4.5 Exploration Bias Immunity of the Output Feedback Learning Algorithms
001453454 504__ $$aIncludes bibliographical references and index.
001453454 5050_ $$aIntro -- Preface -- Contents -- Notation and Acronyms -- 1 Introduction to Optimal Control and Reinforcement Learning -- 1.1 Introduction -- 1.2 Optimal Control of Dynamic Systems -- 1.2.1 Dynamic Programming Method -- 1.2.2 The Linear Quadratic Regulation Problem -- 1.2.3 Iterative Numerical Methods -- 1.3 Reinforcement Learning Based Optimal Control -- 1.3.1 Principles of Reinforcement Learning -- 1.3.2 Reinforcement Learning for Automatic Control -- 1.3.3 Advantages of Reinforcement Learning Control -- Optimality and Adaptivity -- Model-Free Control -- Large Spectrum of Applications
001453454 5058_ $$a1.3.4 Limitations of Reinforcement Learning Control -- 1.3.5 Reinforcement Learning Algorithms -- 1.4 Recent Developments and Challenges in Reinforcement Learning Control -- 1.4.1 State Feedback versus Output Feedback Designs -- 1.4.2 Exploration Signal/Noise and Estimation Bias -- 1.4.3 Discounted versus Undiscounted Cost Functions -- 1.4.4 Requirement of a Stabilizing Initial Policy -- 1.4.5 Optimal Tracking Problems -- 1.4.6 Reinforcement Learning in Continuous-Time -- 1.4.7 Disturbance Rejection -- 1.4.8 Distributed Reinforcement Learning -- 1.5 Notes and References
001453454 5058_ $$a2 Model-Free Design of Linear Quadratic Regulator -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Discrete-Time LQR Problem -- 2.3.1 Iterative Schemes Based on State Feedback -- 2.3.2 Model-Free Output Feedback Solution -- 2.3.3 State Parameterization of Discrete-Time Linear Systems -- 2.3.4 Output Feedback Q-function for LQR -- 2.3.5 Output Feedback Based Q-learning for the LQR Problem -- 2.3.6 Numerical Examples -- 2.4 Continuous-Time LQR Problem -- 2.4.1 Model-Based Iterative Schemes for the LQR Problem -- 2.4.2 Model-Free Schemes Based on State Feedback
001453454 5058_ $$a2.4.3 Model-Free Output Feedback Solution -- 2.4.4 State Parameterization -- 2.4.5 Learning Algorithms for Continuous-Time Output Feedback LQR Control -- 2.4.6 Exploration Bias Immunity of the Output Feedback Learning Algorithms -- 2.4.7 Numerical Examples -- 2.5 Summary -- 2.6 Notes and References -- 3 Model-Free H∞ Disturbance Rejection and Linear Quadratic Zero-Sum Games -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Discrete-Time Zero-Sum Game and H∞ Control Problem -- 3.3.1 Model-Based Iterative Algorithms
001453454 5058_ $$a3.3.2 State Parameterization of Discrete-Time Linear Systems Subject to Disturbances -- 3.3.3 Output Feedback Q-function for Zero-Sum Game -- 3.3.4 Output Feedback Based Q-learning for Zero-Sum Game and H∞ Control Problem -- 3.3.5 A Numerical Example -- 3.4 Continuous-Time Zero-Sum Game and H∞ Control Problem -- 3.4.1 Model-Based Iterative Schemes for Zero-Sum Game and H∞ Control Problem -- 3.4.2 Model-Free Schemes Based on State Feedback -- 3.4.3 State Parameterization -- 3.4.4 Learning Algorithms for Output Feedback Differential Zero-Sum Game and H∞ Control Problem
001453454 506__ $$aAccess limited to authorized users.
001453454 520__ $$aThis monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning (RL) theory, presenting new methods that overcome recent challenges faced by RL. New developments in the design of sensor data efficient RL algorithms are demonstrated that not only reduce the requirement of sensors by means of output feedback, but also ensure optimality and stability guarantees. A variety of practical challenges are considered, including disturbance rejection, control constraints, and communication delays. Ideas from game theory are incorporated to solve output feedback disturbance rejection problems, and the concepts of low gain feedback control are employed to develop RL controllers that achieve global stability under control constraints. Output Feedback Reinforcement Learning Control for Linear Systems will be a valuable reference for graduate students, control theorists working on optimal control systems, engineers, and applied mathematicians.
001453454 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 12, 2022).
001453454 650_0 $$aFeedback control systems.
001453454 650_0 $$aControl theory.
001453454 650_0 $$aReinforcement learning.
001453454 655_0 $$aElectronic books.
001453454 7001_ $$aLin, Zongli,$$d1964-
001453454 77608 $$iPrint version:$$aRizvi, Syed Ali Asad$$tOutput Feedback Reinforcement Learning Control for Linear Systems$$dCham : Springer International Publishing AG,c2022$$z9783031158575
001453454 830_0 $$aControl engineering (Birkhäuser)
001453454 852__ $$bebk
001453454 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-15858-2$$zOnline Access$$91397441.1
001453454 909CO $$ooai:library.usi.edu:1453454$$pGLOBAL_SET
001453454 980__ $$aBIB
001453454 980__ $$aEBOOK
001453454 982__ $$aEbook
001453454 983__ $$aOnline
001453454 994__ $$a92$$bISE