Learning Based Adaptive

Learning-based adaptive control aims to create robust and efficient control systems that can adapt to uncertainties and disturbances in dynamic environments, primarily by integrating machine learning with traditional control methodologies. Current research emphasizes developing distributed and hierarchical control architectures, often employing neural networks (including ODE networks) and meta-learning techniques to handle complex, high-dimensional systems and diverse disturbance types. This approach is significantly impacting robotics, particularly in areas like multi-agent systems and aerial/underwater vehicles, by enabling more adaptable and reliable control in challenging and unpredictable conditions.