A convex approach for NMPC based on second order Volterra series models

This paper presents a novel approach to use second order Volterra series models in nonlinear model predictive control. A common technique in model predictive control is the minimization of a quadratic cost function with respect to the future input sequence. In the case of nonlinear models, the resul...

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Bibliographic Details
Main Authors: Gruber, J K, Alamo, T, Ramírez, D R, Bordons, C, Camacho, E F
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Approximation methods
Computational modeling
Convergence
Cost function
Predictive models
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Summary:This paper presents a novel approach to use second order Volterra series models in nonlinear model predictive control. A common technique in model predictive control is the minimization of a quadratic cost function with respect to the future input sequence. In the case of nonlinear models, the resulting cost function is a possibly non-convex function. The proposed strategy uses quadratic cost functions to approximate the original cost function. For the quadratic cost functions, convexity can be achieved easily by adding a weighting function of the control increments. The approximated convex cost functions are minimized globally by means of an iterative approach with guaranteed convergence. The proposed control strategy is applied to a continuous stirred tank reactor and the control performance is illustrated by experimental results.
ISSN:0191-2216
DOI:10.1109/CDC.2010.5718065