Adaptive spacecraft attitude control with incremental approximate dynamic programming

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This paper presents an adaptive control technique to deal with spacecraft attitude tracking and disturbance rejection problems in the presence of model uncertainties. Approximate dynamic programming has been proposed to solve adaptive, optimal control problems without using accurate systems models. Within this category, linear approximate dynamic programming systematically utilizes a quadratic cost-to-go function and simplifies the design process. Although modelfree and efficient, linear approximate dynamic programming methods are difficult to apply to nonlinear systems or timevarying systems, such as attitude control of spacecraft disturbed by internal liquid sloshing. To deal with this problem, this paper develops a model-free nonlinear self-learning attitude control method based on incremental Approximate Dynamic Programming to enhance the performance of the spacecraft attitude control system. This method combines the advantages of linear approximate dynamic programming and the incremental nonlinear control techniques, and generates a model-free controller for unknown, time-varying dynamical systems. In this paper, two reference tracking algorithms are developed for off-line learning and online learning, respectively. These algorithms are applied to the attitude control of a spacecraft disturbed by internal liquid sloshing. The results demonstrate that the proposed method deals with the unknown, timevarying internal dynamics adaptively while retaining accurate and efficient attitude control.