Longitudinal control of hypersonic vehicles based on direct heuristic dynamic programming using ANFIS

Since the launch of the scramjet, recent years have witnessed a growing interest in the study of airbreathing hypersonic vehicles. Due to its strong coupling characteristics, high nonlinearity, and uncertain parameters, the control of hypersonic vehicle becomes a great challenge. To deal with those design issues, we propose an adaptive learning control method based on direct heuristic dynamic programming (direct HDP), which is used to track the angle of attack despite the presence of bounded uncertain parameters. Inspired by the adaptive critic designs, direct HDP is one of the adaptive dynamic programming (ADP) methods, which is a model-free reinforcement learning algorithm using the online learning scheme to solve dynamic control problems in realistic complex environment. In this paper, this direct HDP method is improved by embedding the fuzzy neural network (FNN) in the controller design to enhance its self-learning ability and robustness. Simulation results are provided to demonstrate the effectiveness of our proposed method.