The load relief control of launch vehicles reduces aerodynamic loads by decreasing the angle of attack. However, existing Active Disturbance Rejection Control (ADRC) methods for load relief do not fully consider elastic effects, which may lead to reduced disturbance estimation accuracy and even compromise system stability. To address this, this paper analyzes the impact of elastic vibration on disturbance estimation and observer gain, and proposes an improvement to suppress elastic vibration. By isolating elastic motion from rigid-body motion, the measured input of the Extended State Observer (ESO) is made to match the observation model, thereby reducing the influence of elasticity on disturbance estimation. Based on this, an open-loop transfer function of the ADRC system for load relief is derived considering elastic vibration, and a set of parameter tuning rules is provided. By properly configuring the bandwidths of the load relief feedback control and the ESO, the tuning process is simplified, while ensuring sufficient stability margins. Simulation and experimental results demonstrate that this method proposed can enhance system stability, while achieving effective disturbance suppression and load relief. Feasibility of the algorithm is validated through hardware-in-the-loop simulations and flight tests on a rocket.

An optimal feedback guidance law with disturbance rejection objective is proposed for endoatmospheric powered descent. This guidance law with an affine form is derived by solving a novel problem called Endoatmospheric Powered Descent Guidance with Disturbance Rejection (Endo-PDG-DR). The key idea of formulating the Endo-PDG-DR problem is dividing disturbances into two parts, modeled and unmodeled disturbances: the modeled disturbance is proactively exploited by augmenting it as a new state of a dynamics model; the unmodeled disturbance is reactively attenuated in terms of its effect on the guidance performance by adjoining a parameterized time-varying quadratic performance index in the proposed optimal guidance problem. A Pseudospectral Differential Dynamic Programming (PDDP) method is developed to solve the Endo-PDG-DR problem, and correspondingly a robust neighboring optimal state feedback law is obtained, which has two synergistic functionalities. One is adaptive optimal steering to accommodate the modeled disturbance, and the other is disturbance attenuation to compensate for the state perturbation effect induced by the unmodeled disturbance. Using the derived feedback guidance law, a disturbance rejection level is quantified, and is correspondingly optimized by designing a quadratic weighting parameter tuning law. The numerical computations of interest are performed within a pseudospectral setting, ensuring polynomial analytical solution, high computational efficiency, and reliable convergence.

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.

Neighboring optimal guidance, a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory, is widely used in the aerospace field due to its high computational efficiency and low resource usage. For more advanced scenarios, the existing methods still have a problem that the guidance accuracy and optimality will seriously degrade when the actual state largely deviates from the nominal trajectory. This is mainly caused by the approximate description of the first-order conditions in terms of total flight time and nonlinear constraints. To address this problem, a higher-order neighboring optimal guidance method is proposed. First, a novel total flight time updating strategy, together with a normalized time scale, is presented that transforms the optimal problem with free total flight time into a more tractable optimal problem with fixed total flight time. Then, using the vector partial derivative method, a higher-order approximation is adopted, instead of the first-order approximation, to accurately describe the nonlinear dynamical and terminal constraints, thus obtaining a polynomially constrained quadratic optimal problem. Finally, to numerically solve the polynomially constrained quadratic optimal problem, a Newton-type iterative algorithm based on the orthogonal decomposition is designed. Through the iterative solution within each guidance period, the corrections to control quantities and total flight time are generated. The proposed method is applied to a launch vehicle orbital injection problem, and simulation results show that it achieves high accuracy of orbital injection and optimality of performance index.