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Adaptive Dynamic Programming for Control
Huaguang Zhang,Derong Liu,Yanhong Luo,Ding Wang +3 more
- 14 Dec 2012
231
TL;DR: Reading adaptive dynamic programming for control is also a way as one of the collective books that gives many advantages, not only for you, but for the other peoples with those meaningful benefits.
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Abstract: No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading adaptive dynamic programming for control is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
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Citations
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
TL;DR: Q-learning and the integral RL algorithm as core algorithms for discrete time (DT) and continuous-time (CT) systems, respectively are discussed, and a new direction of off-policy RL for both CT and DT systems is discussed.
830
Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning
Hamidreza Modares,Frank L. Lewis +1 more
TL;DR: This formulation extends the integral reinforcement learning (IRL) technique, a method for solving optimal regulation problems, to learn the solution to the OTCP, and it also takes into account the input constraints a priori.
624
Adaptive Dynamic Programming for Control: A Survey and Recent Advances
TL;DR: In this article, the adaptive dynamic programming (ADP) with applications in control is reviewed, and the use of ADP to solve game problems, mainly nonzero-sum game problems is elaborated.
500
A Multiagent-Based Consensus Algorithm for Distributed Coordinated Control of Distributed Generators in the Energy Internet
TL;DR: A novel distributed coordinated controller combined with a multiagent-based consensus algorithm is applied to distributed generators in the Energy Internet, which keeps voltage angles and amplitudes consensus, while providing accurate power-sharing and minimizing circulating currents.
Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints
TL;DR: A novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints that is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system.
395