Derivative-free model reference adaptive control of an uncertain satellite system based on RBF neural networks

The capability for orbital satellite systems to compensate for faults and disturbances in uncertain environments is critical. Considering their applications in communication, defense, and scientific observation, the desire to reduce the negative effects due to disturbances and uncertainties has led to the development and implementation of adaptive attitude control theory. Applying this field of control, accuracy, stability, and desired performance is achievable in imperfect situations. This paper proposes the novel application of the derivative-free variation of model reference adaptive control (MRAC) to nonlinear satellite systems for trajectory tracking scenarios. Using a radial basis function (RBF) based neural network to parameterize uncertainties, this method implements a time-varying weight matrix and derivative-free update law, expected to achieve fast adaptation. This method is compared to other MRAC strategies under the same system and environment. The experimental results demonstrate the adaptability of this scheme for satellite systems, able to track trajectories with high accuracy under the presence of common environmental variables and modeling errors.