Nonlinear model-based control with local linear neuro-fuzzy models

2003 ◽  
Vol 72 (11) ◽  
pp. 911-922 ◽  
Author(s):  
A. Fink ◽  
S. Töpfer ◽  
R. Isermann
Keyword(s):  
Nonlinear Model ◽  
Model Based Control ◽  
Fuzzy Models ◽  
Model Based ◽  
Local Linear ◽  
Neuro Fuzzy

Nonlinear model-based control of a binary distillation column

1995 ◽  
Vol 19 ◽  
pp. 519-524
Author(s):  
Antonio Trotta ◽  
Massimiliano Barolo
Keyword(s):  
Nonlinear Model ◽  
Distillation Column ◽  
Model Based Control ◽  
Model Based

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

Nonlinear Model-Based Control of Nonminimum-Phase Processes

1998 ◽  
pp. 115-141 ◽  
Author(s):  
Costas Kravaris ◽  
Michael Niemiec ◽  
Ridvan Berber ◽  
Coleman B. Brosilow
Keyword(s):  
Nonlinear Model ◽  
Nonminimum Phase ◽  
Model Based Control ◽  
Model Based
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