Self-organizing model reference adaptive control for aircraft with enhanced persistent excitation

Abstract

Model Reference Adaptive Control (MRAC) methods are commonly applied in aerospace control systems, yet they face challenges in terms of persistent excitation, adaptation speed, and network structure design. To tackle these issues, this study introduces a novel MRAC method that employs practical techniques, including fast adaptation, experience replay, and network self-organization. Specifically, this study emphasizes three key contributions. First, it introduces a modified MRAC architecture that incorporates a fast adaptation term and a network approximation term to compensate for control errors more effectively. The fast adaptation term, which exhibits higher adaptive speed, enables quick response to dynamic variations and is stored in an experience replay buffer. Additionally, the network term is trained using batches of past samples extracted from the experience replay buffer, rather than relying solely on the current sample. This approach successfully overcomes challenges related to achieving persistent excitation and setting adaptation speed. Secondly, this paper introduces an online method for network self-organization to approximate a global model. This approach autonomously selects basis functions to enhance the organization of the network, leading to improved matching and overall performance. Finally, theoretical proofs and numerical simulations are presented to validate the effectiveness of the proposed techniques and demonstrate the advantages of the method in terms of parameter convergence, learning speed, and control stability.