Near-asteroid spacecraft formation control with prescribed-performance: A dynamic event-triggered reinforcement learning control approach

Abstract

This paper presents an event-triggered approximate optimal tracking control method for near-asteroid spacecraft formation flying systems. Compared with the traditional open-loop optimal control, the proposed solution optimizes the trade-off between tracking performance and online energy consumption by combining prescribed performance control and reinforcement learning. Specifically, a state transformation approach is employed to convert the relative error systems into a form with adjustable performance metrics. Then, a policy iteration algorithm is developed to derive the optimal control policy for the transformed system, which leverages historical data to relax the persistence of excitation conditions. Furthermore, a new Lipschitz-assumption-free dynamic event-triggered mechanism is incorporated to activate the approximate optimal controller only under specific conditions, further reducing the control update frequency. Finally, simulation results show that the update numbers can be lower by 40% compared to the static event-triggered scheme.