A torque-based nonlinear predictive control approach of automotive powertrain by iterative optimization

2012 ◽  
Vol 226 (8) ◽  
pp. 1016-1025 ◽  
Author(s):  
Lin He ◽  
Liang Li ◽  
Liangyao Yu ◽  
Enrong Mao ◽  
Jian Song
Keyword(s):  
Predictive Control ◽  
Control Approach ◽  
Iterative Optimization ◽  
Nonlinear Predictive Control ◽  
Automotive Powertrain

A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

2014 ◽  
Vol 52 (6) ◽  
pp. 802-823 ◽  
Author(s):  
Yiqi Gao ◽  
Andrew Gray ◽  
H. Eric Tseng ◽  
Francesco Borrelli
Keyword(s):  
Predictive Control ◽  
Ground Vehicles ◽  
Control Approach ◽  
Nonlinear Predictive Control

A New Nonlinear Predictive Control Approach Using Hammerstein Models with Compensation Term

2008 ◽  
Vol 41 (2) ◽  
pp. 12171-12176
Author(s):  
Danielle Simone S. Casillo ◽  
André L. Maitelli ◽  
Adhemar B. Fontes
Keyword(s):  
Predictive Control ◽  
Control Approach ◽  
Nonlinear Predictive Control ◽  
Hammerstein Models

scholarly journals On the Anode Pressure and Humidity Regulation in PEM Fuel Cells: a Nonlinear Predictive Control Approach

2015 ◽  
Vol 48 (23) ◽  
pp. 434-439 ◽  
Author(s):  
Noè Rosanas-Boeta ◽  
Carlos Ocampo-Martinez ◽  
Cristian Kunusch
Keyword(s):  
Fuel Cells ◽  
Predictive Control ◽  
Pem Fuel Cells ◽  
Control Approach ◽  
Nonlinear Predictive Control

Constrained predictive control of a SISO nonlinear system based on third-order S-PARAFAC Volterra models

2016 ◽  
Vol 39 (6) ◽  
pp. 907-920 ◽  
Author(s):  
Anis Khouaja ◽  
Tarek Garna ◽  
José Ragot ◽  
Hassani Messaoud
Keyword(s):  
Predictive Control ◽  
Tensor Decomposition ◽  
Nonlinear Process ◽  
Volterra Model ◽  
Continuous Stirred Tank Reactor ◽  
Third Order ◽  
Control Approach ◽  
Nonlinear Predictive Control ◽  
Proposed Model ◽  
Linear And Nonlinear Constraints

This paper is concerned with the identification and nonlinear predictive control approach for a nonlinear process based on a third-order reduced complexity, discrete-time Volterra model called the third-order S-PARAFAC Volterra model. The proposed model is given using the PARAFAC tensor decomposition that provides a parametric reduction compared with the conventional Volterra model. In addition, the symmetry property of the Volterra kernels allows us to further reduce the complexity of the model. These properties allow synthesizing a nonlinear model-based predictive control (NMBPC). Then we construct the general form of a new predictor and we propose an optimization algorithm formulated as a quadratic programming (QP) algorithm under linear and nonlinear constraints. The performance of the proposed third-order S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark such as a continuous stirred-tank reactor system.

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