The problem of model predictive control (MPC) under parametric uncertainties for a
class 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 the
convergence of parameters and the state variables to their true value. The task is posed as
an adaptive model predictive control problem in which the controller is required to steer the
system 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 of
systems. The rst class of system considered is a class of uncertain nonlinear systems with
input to state stability property. Using a generalization of the set-based adaptive estimation
technique, the estimates of the parameters and state are updated to guarantee convergence
to 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 success
of the integration scheme, novel parameter estimation techniques with better convergence
properties are developed. The estimation routine allows exact reconstruction of the systems
unknown parameters in nite-time. The applicability of the identi er to improve upon the
performance of existing adaptive controllers is demonstrated. Then, an adaptive nonlinear
model 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 of
the experimental application demonstrate the ability of the proposed techniques to estimate
the state variables and parameters of an uncertain practical system.Departamento de Ingeniería de Sistemas y AutomáticaMáster en Investigación en Ingeniería de Procesos y Sistemas Industriale
