Approximate dynamic programming based controller design using an improved learning algorithm with application to tracking control of aircraft

The strategy using approximate/adaptive dynamic programming (ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent years. This paper aims at handling an important problem in the design of ADP learning controllers, which is the improvement of learning algorithm for its convergence performance. We analyze ADP controller implementation framework according to the requirement of tracking control task, with emphasis on providing an improved weight-updating gradient descent approach in optimizing connection weights in network structures. A comparison of the proposed method and classic ADP design for tracking and controlling pitch angle of aircraft is presented. It verifies the feasibility in the design of the proposed ADP based controller.