Adaptive dynamic programming in the Hamiltonian-driven framework
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Publication:2094034
DOI10.1007/978-3-030-60990-0_7zbMath1505.90137OpenAlexW3175585886MaRDI QIDQ2094034
Yixin Yin, Donald C. II Wunsch, Yong-Liang Yang
Publication date: 28 October 2022
Full work available at URL: https://doi.org/10.1007/978-3-030-60990-0_7
Cites Work
- Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems
- Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics
- Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning
- Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems
- \(\mathrm{H}_\infty\) control of linear discrete-time systems: off-policy reinforcement learning
- Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance
- A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems
- Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
- Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach
- Primer on Optimal Control Theory
- L/sub 2/-gain analysis of nonlinear systems and nonlinear state-feedback H/sub infinity / control
- An Approximation Theory of Optimal Control for Trainable Manipulators
- Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization
- Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
- Neuro-Dynamic Programming: An Overview and Recent Results
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