Concurrent learning-based trajectory tracking control of a novel multi-node flexible lander for asteroid missions.

Purpose: Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance. Design/methodology/approach: To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance. Findings: Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations. Originality/value: This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed. [ABSTRACT FROM AUTHOR]