Results 201 to 210 of about 63,859 (262)

Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes. [PDF]

Dela J Public Health
Deng Y, Kong Y, Wang X, Li H.
europepmc   +1 more source

Synergistic Intensification of Bioactive Compounds in Germinated Brown Rice via Ultrasonic-Enzymatic Pretreatment: Multi-Objective Modeling Using RSM, ANN-GA, and ANN-PSO. [PDF]

ACS Omega
Chamsai T, Wongwilad K, Wanyo P.
europepmc   +1 more source

Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction. [PDF]

Theor Appl Genet
Yoshioka H, Mary-Huard T, Aubert J, Toda Y, Ohmori Y, Yamasaki Y, Tsujimoto H, Takahashi H, Nakazono M, Takanashi H, Fujiwara T, Tsuda M, Kaga A, Inaba J, Fuji Y, Hirai MY, Nose Y, Kumaishi K, Usui E, Kobori S, Sato T, Narukawa M, Ichihashi Y, Iwata H.   +23 more
europepmc   +1 more source

Real-time decentralized model predictive control for cooperative multi-robot object transport: experimental validation. [PDF]

Sci Rep
Muhammed I, Nada AA, El-Hussieny H.
europepmc   +1 more source

Smart grid inverter control: integrating RNN, model predictive, and adaptive sliding mode controller for optimal harmonic mitigation. [PDF]

Sci Rep
Zeb O, Rehman A, Sultan N, Sherazi HI, Alburidy A, Alrumayh ON.   +5 more
europepmc   +1 more source

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Nonlinear Model Predictive Control with aggregated constraints

Automatica, 2022
In the paper, a nonlinear model predictive control (NMPC) formulation with aggregated constraints approach is introduced. Constraint aggregation function lump the original constraints of the control problem into a reduced set of nonlinear constraints. Then significant saving in the computational footprint of solving the MNPC can be achieved. The effect
Mateus de Freitas Virgilio Pereira, Ilya V. Kolmanovsky, Carlos E. S. Cesnik   +2 more
openaire   +2 more sources

NONLINEAR MODEL PREDICTIVE CONTROL

Chemical Engineering Communications, 1990
Abstract Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed. The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant.
James B Rawlings
exaly   +2 more sources