predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy ̄ows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlin...

This paper presents a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process (GP) model. The GP prior model is trained by artificial bee colony algorithm. The nonlinear function of the objective system is estimated as the predictive mean function of the GP, and the confidence measure of the estimated nonlinear function is given by the predictive covariance...

─While linear model predictive control is popular since the 70s of the past century, only since the 90s there is a steadily increasing interest from control theoreticians as well as control practitioners in nonlinear model predictive control (NMPC). The practical interest is mainly driven by the fact that today’s processes need to be operated under tight performance specifications. At the same ...

The performance of a Model Predictive Control (MPC) algorithm depends on the quality of the derived model. Using a divide-and-conquer strategy process operations were partitioned into several operating regions and within each region, a local linear model was developed to model the process. This set of locally linearized models was simply and effectively combined into a global description of a m...

In this paper, an Output Feedback Model Predictive Control for nonlinear systems is presented. The proposed output feedback control consists of the well known robust controller NCGPC (Nonlinear Continuous Time Generalized Predictive Control) and an open loop observer (a simulated model in parallel) in order to estimate the output derivatives and a regulation filter used to account for plant/mod...

Acknowledgements I wish to express my appreciation and gratitude to my promoters Prof. Asachi " of Iaşi for their help and time over the research period. Also, to Lulu for sharing his experience.

This paper deals with the identification and the control of nonlinear processes described by input -output models, such as parametric Volterra models. In particular, we extend an adaptive predictive algorithm without taking into account constraints. The calculation of the control law can be posed as a thirdorder nonlinear program. The building algorithm is based on a new approach using a convol...

This paper deals with the identification and the control of nonlinear processes described by input -output models, such as parametric Volterra models. In particular, we extend an adaptive predictive algorithm without taking into account constraints. The calculation of the control law can be posed as a thirdorder nonlinear program. The building algorithm is based on a new approach using a convol...