Launch vehicle adaptive flight control with incremental model based heuristic dynamic programming

Abstract

A self-learning controller which makes quick and successful adaptations to new conditions can considerably benefit autonomous operations of launch vehicles. To provide a model-free, adaptive process for optimal control, approximate dynamic programming has been introduced to aerospace engineering. A widely used structure of approximate dynamic programming for nonlinear systems is heuristic dynamic programming. This paper proposes a new method using incremental models in heuristic dynamic programming to improve the online learning capacity. This method generates an adaptive near-optimal controller online without a priori knowledge of the system dynamics or off-line learning of the system model. A comparison is made between the conventional heuristic dynamic programming algorithm and the incremental model based heuristic dynamic programming algorithm by applying them to an online flight control problem with an unknown nonlinear model. The results demonstrate that the incremental model based heuristic dynamic programming method accelerates online learning, improves the precision, and can deal with a wider range of initial states compared to the conventional heuristic dynamic programming method.