Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and...

Dynamic rolling horizon scheduling of waterborne AGVs for inter terminal transport

The demand for transport between terminals within port areas, known as inter terminal transport (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle the dynamically arriving ITT requests and...