RT info:eu-repo/semantics/masterThesis
T1 Adaptive model predictive control
A1 Ebrahim Sadjadi, Mohammad
A2 Universidad de Valladolid. Escuela de Ingenierías Industriales
K1 Control automático
AB The problem of model predictive control (MPC) under parametric uncertainties for aclass of nonlinear systems is addressed. An adaptive identi er is used to estimate the pa-rameters and the state variables simultaneously. The algorithm proposed guarantees theconvergence of parameters and the state variables to their true value. The task is posed asan adaptive model predictive control problem in which the controller is required to steer thesystem to the system setpoint that optimizes a user-speci ed objective function.The technique of adaptive model predictive control is developed for two broad classes ofsystems. The  rst class of system considered is a class of uncertain nonlinear systems withinput to state stability property. Using a generalization of the set-based adaptive estimationtechnique, the estimates of the parameters and state are updated to guarantee convergenceto a neighborhood of their true value.The second involves a method of determining appropriate excitation conditions for nonlin-ear systems. Since the identi cation of the true cost surface is paramount to the successof the integration scheme, novel parameter estimation techniques with better convergenceproperties are developed. The estimation routine allows exact reconstruction of the systemsunknown parameters in  nite-time. The applicability of the identi er to improve upon theperformance of existing adaptive controllers is demonstrated. Then, an adaptive nonlinearmodel predictive controller strategy is integrated to this estimation algorithm in which ro-bustness features are incorporated to account for the e ect of the model uncertainty.To study the practical applicability of the developed method, the estimation of state vari-ables and unknown parameters in a stirred tank process has been performed. The results ofthe experimental application demonstrate the ability of the proposed techniques to estimatethe state variables and parameters of an uncertain practical system.
YR 2013
FD 2013
LK http://uvadoc.uva.es/handle/10324/4953
UL http://uvadoc.uva.es/handle/10324/4953
LA eng
NO Departamento de Ingeniería de Sistemas y Automática
DS UVaDOC
RD 24-nov-2024