Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li's Adaptive Dynamic Programming with Applications in Optimal PDF

By Derong Liu, Qinglai Wei, Ding Wang, Xiong Yang, Hongliang Li

This ebook covers the latest advancements in adaptive dynamic programming (ADP). The textual content starts off with a radical history overview of ADP to ensure that readers are sufficiently conversant in the basics. within the middle of the ebook, the authors deal with first discrete- after which continuous-time structures. assurance of discrete-time structures begins with a extra common type of worth generation to illustrate its convergence, optimality, and balance with entire and thorough theoretical research. A extra sensible kind of price generation is studied the place price functionality approximations are assumed to have finite blunders. Adaptive Dynamic Programming additionally info one other street of the ADP process: coverage new release. either easy and generalized kinds of policy-iteration-based ADP are studied with whole and thorough theoretical research by way of convergence, optimality, balance, and mistake bounds. between continuous-time platforms, the regulate of affine and nonaffine nonlinear structures is studied utilizing the ADP method that's then prolonged to different branches of keep an eye on conception together with decentralized keep an eye on, powerful and assured expense keep an eye on, and online game concept. within the final a part of the publication the real-world importance of ADP concept is gifted, targeting 3 program examples constructed from the authors’ work:

• renewable strength scheduling for clever energy grids;• coal gasification tactics; and• water–gas shift reactions.
Researchers learning clever keep an eye on tools and practitioners trying to practice them within the chemical-process and power-supply industries will locate a lot to curiosity them during this thorough remedy of a sophisticated method of control.

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Additional info for Adaptive Dynamic Programming with Applications in Optimal Control

Sample text

2, 6, 7, 15, 21, 22, 36, 39, 50, 55, 65, 72, 73, 78, 79, 83, 90, 91, 104, 125–128, 137]). In the literature, there are several synonyms used for “adaptive critic designs” including “approximate dynamic programming” [40, 76, 90, 128], “asymptotic dynamic programming” [83], “adaptive dynamic programming” [72, 73, 139], “neuro-dynamic programming” [12], “neural dynamic programming” [133], “relaxed dynamic programming” [46, 81], and “reinforcement learning” [14, 40, 98, 106, 127]. No matter what we call it, in all these cases, the goal is to approximate the solutions of dynamic programming.

Is applied to the system with when the optimal control sequence uk+1 initial state xk+1 . Note that the optimal control sequence depends on xk+1 . 1). According to Bellman, the optimal cost from time k on is equal to J ∗ (xk ) = min{U(xk , uk ) + γ J ∗ (xk+1 )} = min{U(xk , uk ) + γ J ∗ (F(xk , uk ))}. , uk∗ = arg min{U(xk , uk ) + γ J ∗ (xk+1 )}. 4) is the principle of optimality for discrete-time systems. Its importance lies in the fact that it allows one to optimize over only one control vector at a time by working backward in time.

The background of dynamic programming and Markov decision processes is given, and meanwhile the phenomenon of the curse of dimensionality is discussed. A detailed description on how to model a dynamic program and some important algorithmic strategies are presented next. , modeling real applications, the interface with stochastic approximation methods, techniques for approximating general value functions, and a more in-depth presentation of ADP algorithms for finite- and infinite-horizon applications are provided, respectively.

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