Title
Online policy iteration based algorithms to solve the continuous-time infinite horizon optimal control problem
Abstract
In this paper we discuss two online algorithms based on policy iterations for learning the continuous-time (CT) optimal control solution when nonlinear systems with infinite horizon quadratic cost are considered. For the first time we present an online adaptive algorithm implemented on an actor/critic structure which involves synchronous continuous-time adaptation of both actor and critic neural networks. This is a version of generalized policy iteration for CT systems. The convergence to the optimal controller based on the novel algorithm is proven while stability of the system is guaranteed. The characteristics and requirements of the new online learning algorithm are discussed in relation with the regular online policy iteration algorithm for CT systems which we have previously developed. The latter solves the optimal control problem by performing sequential updates on the actor and critic networks, i.e. while one is learning the other one is held constant. In contrast, the new algorithm relies on simultaneous adaptation of both actor and critic networks. To support the new theoretical result a simulation example is then considered.
Year
DOI
Venue
2009
10.1109/ADPRL.2009.4927523
Nashville, TN
Keywords
Field
DocType
continuous time systems,infinite horizon,neurocontrollers,optimal control,stability,actor-critic structure,continuous-time infinite horizon optimal control problem,critic neural networks,infinite horizon quadratic cost,nonlinear systems,online learning algorithm,online policy iteration based algorithms,stability
Convergence (routing),Approximation algorithm,Online algorithm,Mathematical optimization,Optimal control,Nonlinear system,Quadratic cost,Infinite horizon,Artificial neural network,Mathematics
Conference
ISBN
Citations 
PageRank 
978-1-4244-2761-1
7
1.07
References 
Authors
8
3
Name
Order
Citations
PageRank
Kyriakos G. Vamvoudakis1274.74
Draguna Vrabie214111.21
FRANK L. LEWIS35782402.68