Dynamic optimization and optimal control problems form the backbone of numerous applications in engineering, economics and the natural sciences. These methodologies involve determining a time-varying ...

Optimal control theory seeks to determine control strategies that drive dynamical systems to meet performance objectives, while mixed-integer optimisation incorporates both continuous and discrete ...

The goal is to implement, analyze, and compare various stochastic control techniques—from classical methods to modern machine learning approaches—with applications to real-world problems in portfolio ...

Differential equations and systems analysis. Undergraduate controls and/or signal processing course would satisfy this requirement. A graduate-level systems course is also helpful, but not necessary.

Applying model-predictive methods and a continuous process-control framework to a continuous tablet-manufacturing process. Currently, there is a high level of interest in the pharmaceutical industry ...

Abstract: We study how high charging rate demands from electric vehicles (EVs) in a power distribution grid may collectively cause poor dynamic performance, and propose a price incentivization ...

Achieving cost-competitiveness for green hydrogen produced via water electrolysis using intermittent renewable energy sources remains a significant challenge. Researchers from LUT University in ...

A Python framework for trajectory and design optimization using optimal control. MAPTOR simultaneously optimizes system parameters and trajectories for vehicles, robots, spacecraft, and other dynamic ...

Akamas, the autonomous optimization platform, today announced the General Availability of Akamas Insights, the newest module of the Akamas Platform. Initially launched in beta earlier this year, ...