Approximate Dynamic Programming Strategy for Dual Adaptive Control

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 process with an unknown gain is used for illustration. Copyright c ©2005 IFAC