A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) problem. The main benefits of this strategy are that convergence is still guaranteed and good economic performances are obtained, according to several simulation scenarios. The formulation, however, is developed only for the nominal case, which significantly reduces its applicability. In this work, an extension of the gradient-based MPC to explicitly account for disturbances is made. The resulting r...
Model Predictive Control (MPC) is the most used advanced control strategy in the industries, mainly ...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
In industrial practice, the optimal steady-state operation of continuous-time processes is typically...
In the process industries model predictive controllers (MPC) have the task of controlling the plant ...
This paper studies a simplified methodology to integrate the real time optimization (RTO) of a conti...
Here, we study the stable integration of real time optimization (RTO) with model predictive control ...
Model predictive control (MPC) is a modern control methodology that is based on the repetitive solut...
O propósito desta Tese é desenvolver controladores preditivos (MPC) com garantia de estabilidade e q...
Model predictive control (MPC) designates a control method based on the model. This method is suitab...
The problem of cooperation of Model Predictive Control (MPC) algorithms with steady-state economic o...
Model predictive control (MPC) is applied to a physical pendulum system consisting of a pendulum and...
Here, the implementation of the gradient-based Economic MPC (Model Predictive Control) in an industr...
Designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the opt...
This paper presents a real-time implementation of the proximal gradient method (PGM) in a model pred...
Nowadays, real-time optimization (RTO) and nonlinear as well as linear model predictive control (MPC...
Model Predictive Control (MPC) is the most used advanced control strategy in the industries, mainly ...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
In industrial practice, the optimal steady-state operation of continuous-time processes is typically...
In the process industries model predictive controllers (MPC) have the task of controlling the plant ...
This paper studies a simplified methodology to integrate the real time optimization (RTO) of a conti...
Here, we study the stable integration of real time optimization (RTO) with model predictive control ...
Model predictive control (MPC) is a modern control methodology that is based on the repetitive solut...
O propósito desta Tese é desenvolver controladores preditivos (MPC) com garantia de estabilidade e q...
Model predictive control (MPC) designates a control method based on the model. This method is suitab...
The problem of cooperation of Model Predictive Control (MPC) algorithms with steady-state economic o...
Model predictive control (MPC) is applied to a physical pendulum system consisting of a pendulum and...
Here, the implementation of the gradient-based Economic MPC (Model Predictive Control) in an industr...
Designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the opt...
This paper presents a real-time implementation of the proximal gradient method (PGM) in a model pred...
Nowadays, real-time optimization (RTO) and nonlinear as well as linear model predictive control (MPC...
Model Predictive Control (MPC) is the most used advanced control strategy in the industries, mainly ...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
In industrial practice, the optimal steady-state operation of continuous-time processes is typically...