Although nonlinear model predictive control (NMPC) might be the best choice for a
nonlinear plant, it is still not widely used. This is mainly due to the computational burden
associated with solving online a set of nonlinear differential equations and a nonlinear
dynamic optimization problem in real time. This thesis is concerned with strategies aimed at
reducing the computational burden involved in different stages of the NMPC such as
optimization problem, state estimation, and nonlinear model identification. A major part of …

Although nonlinear model predictive control (NMPC) might be the best choice for a
nonlinear plant, it is still not widely used. This is mainly due to the computational burden
associated with solving a set of nonlinear differential equations and a nonlinear dynamic
optimization problem. In this work, a new NMPC algorithm based on nonlinear least square
optimization is proposed. In the new algorithm, the residual Jacobian matrix is efficiently
calculated from the model sensitivity functions without extra integrations. Recently …