Neural network-based optimization and control methods, often referred to as black-box
approaches, are increasingly gaining attention in energy and manufacturing systems …

High-speed parallel manipulators are characterized by fast sampling rates and challenges
owing to the presence of constraints, high nonlinearities, uncertainties, and fast dynamics …

The increasing need for capacity has led the railway industry to explore new train control
systems based on a concept called virtual coupling. Inspired by the platooning of …

Leveraging Input Convex Neural Networks (ICNNs), ICNN-based Model Predictive Control
(MPC) successfully attains globally optimal solutions by upholding convexity within the MPC …

This paper presents a dynamical recurrent neural network‐(RNN‐) based model predictive
control (MPC) structure for the formation flight of multiple unmanned quadrotors. A …

Computational efficiency and non-adversarial robustness are critical factors in process
modeling and optimization for real-world engineering applications. Yet, conventional neural …

Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function
in individuals with neurological impairments. However, the rapid onset of FES-induced …

During functional electrical stimulation (FES), muscle force saturation and a user's tolerance
levels of stimulation intensity limit a controller's ability to deliver the desired amount of …

This paper presents an equivalent consumption minimization strategy (ECMS) based on
model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of …

For further reducing the fuel consumption of the series hybrid loaders (SHLs) with input
disturbances, this paper develops a robust heuristic dynamic programming (HDP) online …