Computationally efficient nonlinear predictive control based on neural Wiener models

2010 ◽  
Vol 74 (1-3) ◽  
pp. 401-417 ◽  
Author(s):  
Maciej ławryńczuk
Keyword(s):  
Predictive Control ◽  
Computationally Efficient ◽  
Nonlinear Predictive Control ◽  
Wiener Models

scholarly journals Nonlinear predictive control based on neural multi-models

2010 ◽  
Vol 20 (1) ◽  
pp. 7-21 ◽  
Author(s):  
Maciej Ławryńczuk ◽  
Piotr Tatjewski
Keyword(s):  
Predictive Control ◽  
Prediction Error ◽  
Control Policy ◽  
External Input ◽  
Multi Layer Perceptron ◽  
Computationally Efficient ◽  
Nonlinear Predictive Control ◽  
Nonlinear Optimisation ◽  
On Line ◽  
Auto Regressive

Nonlinear predictive control based on neural multi-modelsThis paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.

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.

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