🎛️🧠 How can control systems learn to make optimal decisions on their own—balancing theoretical guarantees with real-world performance in everything from microgrids to smart home energy management? 📘 𝙄𝙩𝙚𝙧𝙖𝙩𝙞𝙫𝙚 𝘼𝙙𝙖𝙥𝙩𝙞𝙫𝙚 𝘿𝙮𝙣𝙖𝙢𝙞𝙘 𝙋𝙧𝙤𝙜𝙧𝙖𝙢𝙢𝙞𝙣𝙜 𝙛𝙤𝙧 𝙎𝙚𝙡𝙛-𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙊𝙥𝙩𝙞𝙢𝙖𝙡 𝘾𝙤𝙣𝙩𝙧𝙤𝙡 by Qinglai Wei, Ruizhuo Song, and Hongyang Li introduces iterative adaptive dynamic programming (IADP) theory from a control systems perspective—presenting both advanced theoretical analyses and the most recent practical applications. Volume 2 of the Series on Deep Learning Neural Networks, this monograph highlights real-world demonstrations in residential energy systems, showing the strong performance of iterative ADP methods. 🔎 Why this book is essential reading: 📖 1. Principles of Adaptive Dynamic Programming 🧱 Foundational principles of adaptive dynamic programming 🎯 Self-learning optimal control under uncertainty 🔬 Setting the stage for iterative refinement 🔁 2. Discrete-Time Iterative Methods 📊 Discrete-time local value iterative ADP ✅ Admissibility and termination analysis 🔄 Discrete-time local policy iteration ADP ⏱️ 3. Continuous-Time and Game-Theoretic Extensions 🕒 Continuous-time time-varying policy iteration ♟️ ADP for discrete-time zero-sum games 🎮 Model-free optimal control for unknown nonlinear multi-player non-zero-sum games 🤝 4. Distributed and Fault-Tolerant Control 🌐 Continuous-time distributed policy iteration for multi-controller nonlinear systems 🛡️ Data-based fault-tolerant control via distributed policy iteration 🔗 Coordination across multi-controller architectures 🏠 5. Smart Energy and Real-World Applications 🔋 Dual iterative Q-learning for optimal battery management in residential environments ⚡ Mixed iterative ADP for optimal battery energy control in microgrids 🏡 Error-tolerant ADP for renewable home energy scheduling and actor-critic learning in smart home energy management 🌐 Explore the book here: worldscientific.com/worldsci… 💡 Ideal for researchers, professionals, academics, and undergraduate and graduate students in control engineering—as well as practitioners working at the intersection of optimal control, reinforcement learning, and energy management. 👉 Quote 𝐖𝐒𝐓𝐖𝐓𝐑𝟑𝟎 at checkout to enjoy 𝟑𝟎% off your purchase now! #AdaptiveDynamicProgramming #IterativeADP #SelfLearningControl #OptimalControl #PolicyIteration #ValueIteration #ZeroSumGames #NonZeroSumGames #ActorCriticLearning #QLearning #ModelFreeControl #DistributedControl #FaultTolerantControl #BatteryManagement #MicrogridControl #SmartHomeEnergyManagement #ReinforcementLearningControl #NonlinearSystems

Title: Learning to Boost the Performance of Stable Nonlinear Systems Authors: Luca Furieri; Clara Lucía Galimberti; Giancarlo Ferrari-Trecate Date: 12 August 2024 Link: ieeexplore.ieee.org/document… #controlsystems #distributedcontrol #optimalcontrol

#mdpienergies #highlycitedpaper A Review of Distributed Secondary Control Architectures in Islanded-Inverter-Based Microgrids 👉 ow.ly/ZmS950RpWnA #decentralizedcontrol #distributedcontrol #microgrids #powersystem #renewablesources #secondarycontrol

Title: Global Performance Guarantees for Localized Model Predictive Control Authors: Jing Shuang Li; Carmen Amo Alonso Date of Publication: 24 Aug 2023 Link: ieeexplore.ieee.org/document… #predictivecontrol #optimization #distributedcontrol #convergence #largescalesystems

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