Enhancing Attitude Tracking With Self-Learning Control Using Tanh-Type Learning Intensity

Abstract

This paper investigates the attitude tracking control problem for spacecraft. A tanh-type self-learning control (TSLC) approach with variable learning intensity (VLI) is proposed, which avoids saturation while overcoming previous algorithms’ long response time disadvantage. Unlike the previously introduced VLI method, the enhanced TSLC does not tweak the learning intensity based on the previous controller output. Instead, it relates learning intensity to an intermediate variable directly related to the system state and tunes the learning intensity using a tanh-type function. Since the system state reflects the tracking error in real-time, the transformed tanh-type function has a higher decay rate than the exponential function, which not only significantly reduces the saturation response but also improves the response speed and achieves higher steady-state accuracy. Simulation proved TSLC’s superiority, considering adverse actuator factors such as dead zone, bias torque, and saturation. The proposed approach has also been validated on the Quanser helicopter platform, confirming its better performance. Note to Practitioners—Unlike spacecraft control algorithms based on observers, adaptive methods, or neural networks, the proposed algorithm is simpler in control structure and consumes fewer computational resources. With the self-learning mechanism, the desired control accuracy can be achieved without needing an accurate spacecraft dynamics model. It is model-free and exceptionally easy to implement.