Online-learning control with weakened saturation response to attitude tracking: A variable learning intensity approach

Abstract This brief investigates the problem of attitude tracking control using a variable learning intensity (VLI) online-learning control (OLC) scheme. The unique specialty of the proposed VLI-OLC scheme is that it achieves control performance enhancement via learning the previous control information online. The implementation is performed by a simple algebraic equation, which achieves decent control robustness while avoiding a complex control design and saving computational resources. The OLC's saturation response caused by the extensive system error during execution is noticeably weakened by introducing a VLI approach. Compatibility with previous algorithms can be guaranteed. The application example shows that control performance and saturation reduction are guaranteed concurrently.