Add to ORKGAbstract: An approximate dynamic programming (ADP) strategy for a dual adaptive control problem is presented. An optimal control policy of a dual adaptive control problem can be derived by solving a stochastic dynamic programming problem, which is computationally intractable using conventional solution methods that involve sampling of a complete hyperstate space. To solve the problem in a computationally amenable manner, we perform closed-loop simulations with different control policies to generate a data set that defines a subset of a hyperstate within which the Bellman equation is iterated. A local approximator with a penalty function is designed for estimation of cost-to-go values over the continuous hyperstate space. An integrating proc...
Abstract—Unlike the many soft computing applications where it suffices to achieve a “good approximat...
This paper deals with approximate value iteration (AVI) algorithms applied to discounted dynamic pro...
Adaptive Dynamic Programming constitutes a potentially powerful approach to optimal control. An appr...
In optimal control of uncertain systems, lack of crucial information about the system can lead to un...
Computing the exact solution of an MDP model is generally difficult and possibly intractable for rea...
This brief studies the stochastic optimal control problem via reinforcement learning and approximate...
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins...
The general approach to adaptive and dual control is to formulate an optimal stochastic control prob...
Dual control explicitly addresses the problem of trading off active exploration and exploitation in ...
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond th...
This paper gives an overview of different techniques for solving the dual control problem. The optim...
The Stochastic Dual Dynamic Programming (SDDP) algorithm has become one of the main tools to address...
Unlike the many soft computing applications where it suffices to achieve a good approximation most ...
This thesis studies approximate optimal control of nonlinear systems. Particular attention is given ...
The curse of dimensionality gives rise to prohibitive computational requirements that render infeasi...
Abstract—Unlike the many soft computing applications where it suffices to achieve a “good approximat...
This paper deals with approximate value iteration (AVI) algorithms applied to discounted dynamic pro...
Adaptive Dynamic Programming constitutes a potentially powerful approach to optimal control. An appr...
In optimal control of uncertain systems, lack of crucial information about the system can lead to un...
Computing the exact solution of an MDP model is generally difficult and possibly intractable for rea...
This brief studies the stochastic optimal control problem via reinforcement learning and approximate...
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins...
The general approach to adaptive and dual control is to formulate an optimal stochastic control prob...
Dual control explicitly addresses the problem of trading off active exploration and exploitation in ...
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond th...
This paper gives an overview of different techniques for solving the dual control problem. The optim...
The Stochastic Dual Dynamic Programming (SDDP) algorithm has become one of the main tools to address...
Unlike the many soft computing applications where it suffices to achieve a good approximation most ...
This thesis studies approximate optimal control of nonlinear systems. Particular attention is given ...
The curse of dimensionality gives rise to prohibitive computational requirements that render infeasi...
Abstract—Unlike the many soft computing applications where it suffices to achieve a “good approximat...
This paper deals with approximate value iteration (AVI) algorithms applied to discounted dynamic pro...
Adaptive Dynamic Programming constitutes a potentially powerful approach to optimal control. An appr...