A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systems
An online adaptive reinforcement learning-based solution is developed for the infinite-
horizon optimal control problem for continuous-time uncertain nonlinear systems. A novel …
horizon optimal control problem for continuous-time uncertain nonlinear systems. A novel …
Online adaptive algorithm for optimal control with integral reinforcement learning
KG Vamvoudakis, D Vrabie… - International Journal of …, 2014 - Wiley Online Library
In this paper, we introduce an online algorithm that uses integral reinforcement knowledge
for learning the continuous‐time optimal control solution for nonlinear systems with infinite …
for learning the continuous‐time optimal control solution for nonlinear systems with infinite …
Neural‐network‐based online optimal control for uncertain non‐linear continuous‐time systems with control constraints
In this study, an online adaptive optimal control scheme is developed for solving the infinite‐
horizon optimal control problem of uncertain non‐linear continuous‐time systems with the …
horizon optimal control problem of uncertain non‐linear continuous‐time systems with the …
Adaptive interleaved reinforcement learning: Robust stability of affine nonlinear systems with unknown uncertainty
This article investigates adaptive robust controller design for discrete-time (DT) affine
nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved …
nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved …
Guaranteed cost neural tracking control for a class of uncertain nonlinear systems using adaptive dynamic programming
This paper presents an adaptive dynamic programming-based guaranteed cost neural
tracking control algorithm for a class of continuous-time matched uncertain nonlinear …
tracking control algorithm for a class of continuous-time matched uncertain nonlinear …
Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks
This paper presents an online policy iteration (PI) algorithm to learn the continuous-time
optimal control solution for unknown constrained-input systems. The proposed PI algorithm …
optimal control solution for unknown constrained-input systems. The proposed PI algorithm …
Reinforcement learning-based optimal stabilization for unknown nonlinear systems subject to inputs with uncertain constraints
This article presents a novel reinforcement learning strategy that addresses an optimal
stabilizing problem for unknown nonlinear systems subject to uncertain input constraints …
stabilizing problem for unknown nonlinear systems subject to uncertain input constraints …
Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics
An online adaptive optimal control is proposed for continuous-time nonlinear systems with
completely unknown dynamics, which is achieved by developing a novel identifier-critic …
completely unknown dynamics, which is achieved by developing a novel identifier-critic …
Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints
In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-
horizon optimal control problem of constrained-input continuous-time nonlinear systems in …
horizon optimal control problem of constrained-input continuous-time nonlinear systems in …
Asymptotically stable adaptive–optimal control algorithm with saturating actuators and relaxed persistence of excitation
KG Vamvoudakis, MF Miranda… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
This paper proposes a control algorithm based on adaptive dynamic programming to solve
the infinite-horizon optimal control problem for known deterministic nonlinear systems with …
the infinite-horizon optimal control problem for known deterministic nonlinear systems with …