Improved guidance algorithm considering terminal attitude constraints for spacecrafts via optimal control theory

Abstract

Summary Motivated by new aerospace applications, the condition of minimum‐energy achieving high accuracy of both terminal orbital injection and attitude angle is required for better navigation and observation. The stability of guidance command also needs to be improved considering the control system. In this article, an optimal guidance algorithm with an advanced numerical method of spacecrafts is proposed. In order to ensure the continuity of the attitude angle and its terminal constraints, the thrust vector is treated as the state variable in the optimal control problems, and the rate of attitude angle change is regarded as the control variable. Then the optimal ascent problem of spacecrafts based on a nondimensional dynamical model is derived in detail, including performance index considering energy consumption, optimal conditions, and terminal conditions. To improve the computational efficiency of optimal ascent problems, a numerical method and a solution strategy are proposed. Simulation results show that the terminal attitude angle error of proposed method is much less than that of the traditional guidance method, and the continuity and stability of the guidance command is also better, which demonstrates the high accuracy and strong adaptability of the guidance algorithm developed in this article.

Control for hybrid systems: Applications and methods for adaptation and optimality

“…New stability conditions and control designs based on numerical methods, including linear matrix inequalities and gradient based optimization methods, are introduced to address time delays, parametric uncertainties, and the nonlinearities involved in the switching dynamics with stability and performance guarantees. The second group of articles, 5‐14 constituting the major category of this issue, applies adaptive control, optimization methods, and hybrid system theories to various practical fields, ranging from UAVs, AGVs, robotics, and water systems to batch processes. The last group 15,16 considers distributed cyber physical systems, which deals with parametric uncertainties, communication constraints, and signal estimations by resorting to adaptive control and fast Kalman filtering.…”