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Springer Reinforcement Learning for Optimal Feedback Control - Lyapunov-Based Approach

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Description

Reinforcement Learning for Optimal Feedback Control explores model-based and data-driven reinforcement learning methods designed to solve optimal control problems in nonlinear deterministic dynamical systems. This Springer text develops data-driven techniques for identifying system models in real-time, enabling learning under uncertainty. The book demonstrates the practical advantages of incorporating model-based approaches and leveraging previously recorded data through comprehensive simulations and experiments. By concentrating on deterministic systems, the work provides rigorous Lyapunov-based analysis that bridges theory and application. Ideal for engineers, researchers, and students seeking to understand how reinforcement learning can optimise feedback control in complex nonlinear systems, this comprehensive guide combines theoretical foundations with practical methodologies for advanced control applications.

Key Features

Reinforcement Learning for Optimal Feedback Control covers both model-based and data-driven methods for solving optimal control problems.

The book develops real-time system identification techniques to enable effective learning under uncertainty conditions.

Lyapunov-based analysis provides rigorous theoretical foundation for stability and performance guarantees in nonlinear control systems.

Practical simulations and experiments throughout demonstrate how model-based approaches and recorded data improve control performance.

Focus on deterministic systems allows for detailed mathematical treatment suitable for advanced engineering and research applications.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
28 May 2018
Listed Since
21 February 2018

Barcode

No barcode data available

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