Model Reference Adaptive

Model Reference Adaptive Control (MRAC) aims to make a system's behavior match a desired reference model, even in the presence of uncertainties or disturbances. Current research focuses on improving MRAC's robustness and performance using neural networks, particularly shallow ReLU networks with randomly initialized weights, and incorporating techniques like reachability analysis and reinforcement learning to enhance safety and adaptability. These advancements are significant for applications requiring precise control in unpredictable environments, such as autonomous vehicles and robotics, by enabling more reliable and safer operation in the face of model inaccuracies and external disturbances.