.png){kind=icon}
Return
Nonlinear model predictive control based on Nelder Mead optimization method
.png){kind=icon}
PRE
AI
DOI：10.1007/s11071-017-3544-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a model predictive control (MPC) scheme based on Hammerstein model is carried on. The use of such nonlinear models complicates the implementation of the MPC in terms of computational time and burden since a nonlinear and so a nonconvex optimization problem will result. The Nelder Mead (NM) algorithm, as a free derivative method, is used to solve the resulting optimization problem. NM algorithm proves its efficiency in terms of computation time and global optimum seeking that can be successfully exploited especially with fast dynamic systems. A comparative study between the NM algorithm and the gradient-based method (GBM) based on computation time is established. The efficiency of the NM algorithm is illustrated with SISO and MIMO examples compared to GBM algorithm.
Keywords:
Model predictive control
Hammerstein model
Optimization
Nelder Mead algorithm
Gradient method
Computation time
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
Organization
Cited Papers
ShareSaveShareSave
Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
ShareSave
ShareSave
View more