Integrated guidance and control of hypersonic flight vehicle with coordinated mission requirement and input constraint

An integrated adaptive guidance and control algorithm is investigated for the hypersonic flight vehicle (HFV) under the objection of coordinated for mission requirement and control capability. The design pressure to avoid input saturation is placed in the guidance loop by analyzing the hard restriction on the fuel-air equivalence ratio (FER) and the acceleration mission objective. Using the concept of model self-shaping, a loss function is established to achieve the self-optimization of the reference model bandwidth. The reference trajectory of the velocity slows down as the FER approaches its maximum value and becomes faster as the FER demand decreases. The backstepping frame is applied in the attitude control system, and the dynamics uncertainty with the unknown aerodynamic parameters is constructed using neural network. The Karush–Kuhn–Tucker multiplier is used as a link parameter of the guidance and control integrated framework and is introduced into the adaptive update law. An adaptive algorithm is proposed with the estimating evaluation derived from the serial-parallel estimation model. A HFV simulation test with FER constraint shows that the adaptive integrated guidance and control can follow the desired trajectory with high tracking accuracy, and the control input can be kept within a feasible value.