Blar i Institutt for teknisk kybernetikk på emneord "VDP::Electro-technical sciences: 540"
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Connecting sensitivity, identifiability and interpretability of a glucose minimal model
(Chapter, 2024)Mathematical models have increased their applications in physiology, control and systems science, and biomedical engineering because they offer the opportunity to examine the structure and behavior of complex physiological ... -
Discussion on the mode mixing in wave energy control systems using the Hilbert-Huang transform
(Peer reviewed; Journal article, 2019)A great improvement in the absorption of energy of a wave energy converter (WEC) is obtained with a time-varying power take-off (PTO) damping over a constant damping. In a passive control scheme based on the Hilbert-Huang ... -
Evaluation of Energy Transfer Efficiency for Shore-to-Ship Fast Charging Systems
(Peer reviewed; Journal article, 2020)Shore-to-ship charging systems are usually designed based on various operational and design parameters including the onboard power and propulsion requirements, available charging times, and the capability of local power ... -
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models
(Chapter, 2019)Nonlinear model predictive control (NMPC) is an efficient control approach for multivariate nonlinear dynamic systems with process constraints. NMPC does however require a plant model to be available. A powerful tool to ... -
Output feedback stochastic nonlinear model predictive control for batch processes
(Journal article; Peer reviewed, 2019)Batch processes play a vital role in the chemical industry, but are difficult to control due to highly nonlinear behaviour and unsteady state operation. Nonlinear model predictive control (NMPC) is therefore one of the few ... -
Output feedback stochastic nonlinear model predictive control of a polymerization batch process
(Chapter; Peer reviewed, 2019)Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant ... -
Stochastic Nonlinear Model Predictive Control Using Gaussian Processes
(Chapter, 2018)Model predictive control is a popular control approach for multivariable systems with important process constraints. The presence of significant stochastic uncertainties can however lead to closed-loop performance and ...