Knowledge-based integrated optimization design of agile imaging satellites' attitude controller and vibration isolator

摘要

The integrated design of modern agile imaging satellites' systems demands multidisciplinary optimization to ensure the satisfaction of image quality and acquisition efficiency requirements. Beyond the scope of traditional integrated design methods that optimize parameters, the policy to vary parameters over time is required to be optimized especially for changing environments and objectives. Though modern optimization methods provide powerful tools, knowledge about the dynamic system is indispensable to guarantee credibility for truly intelligent integrated design. This paper proposes an integrated control and structural optimization design scheme to optimize variables of the full control-structure system in order to meet conflicting constraints on the attitude control stability requirement and the vibration isolation requirement. To reduce the workload of the iterative design, a safe reinforcement learning algorithm is proposed to learn the time-varying parameters of the camera's vibration isolator and the attitude controller. Our design philosophy is to combine priori knowledge about the non-collocation control stability into the reinforcement learning optimization to safely optimize the control-structure design. The constraint satisfaction is achieved with an improved safety layer technique applied to the output policy of the reinforcement learning agent, which solves a nonlinear constraint programming problem to timely confine learned parameters. We exemplify the proposed method on a push-broom imaging case and a backward-forward slewing case, successfully optimizing the effective imaging time under constraints.