An Efficient Nonlinear Predictive Control Algorithm with Neural Models Based on Multipoint On-Line Linearisation

2007 ◽  
Author(s):  
Maciej Lawrynczuk
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Neural Models ◽  
Nonlinear Predictive Control ◽  
On Line

scholarly journals Efficient Nonlinear Predictive Control Based on Structured Neural Models

2009 ◽  
Vol 19 (2) ◽  
pp. 233-246 ◽  
Author(s):  
Maciej Ławryńczuk
Keyword(s):  
Predictive Control ◽  
Neural Model ◽  
External Input ◽  
Computationally Efficient ◽  
Neural Models ◽  
Loop Control ◽  
Nonlinear Predictive Control ◽  
On Line ◽  
Auto Regressive ◽  
Narx Models

Efficient Nonlinear Predictive Control Based on Structured Neural ModelsThis paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models. The described suboptimal MPC algorithm needs solving on-line only a quadratic programming problem. Nevertheless, it gives closed-loop control performance similar to that obtained in fully-fledged nonlinear MPC, which hinges on online nonconvex optimisation. In order to demonstrate the advantages of structured models as well as the accuracy of the suboptimal MPC algorithm, a polymerisation reactor is studied.

scholarly journals Rollover Prevention System Dedicated to ATVs on Natural Ground

2012 ◽  
Vol 162 ◽  
pp. 505-514 ◽  
Author(s):  
Mathieu Richier ◽  
Roland Lenain ◽  
Benoit Thuilot ◽  
Christophe Debain
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Load Transfer ◽  
Low Cost ◽  
Lateral Load ◽  
Dynamical Model ◽  
Rollover Prevention ◽  
Perception System ◽  
Prevention System ◽  
On Line

In this paper, an algorithm dedicated to light ATVs, which estimates and anticipates the rollover, is proposed. It is based on the on-line estimation of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. The LLT is computed thanks to a dynamical model split into two 2D projections. Relying on this representation and a low cost perception system, an observer is proposed to estimate on-line the terrain properties (grip conditions and slope), then allowing to deduce accurately the risk of instability. Associated to a predictive control algorithm, based on the extrapolation of riders action, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions.

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