A true adaptive nonlinear model predictive control (MPC) algorithm must address the issue of robustness to model uncertainty while the estimator is evolving. Unfortunately, this may not be achieved without introducing extra degree of conservativeness and/or computational complexity in the controller calculations. To attenuate this problem, we employ a finite time identifier and propose an adapt...

Model predictive control (MPC) is a very popular controller design method in the process industry. One of the main advantages of MPC is that it can handle constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. Recently we have extended this framework to max-plus-linear discrete event systems. In this paper we further explore this topic. More specifically, we focus ...

This paper deals with a reusable simulation computer code (MPC@CB). The original program was developed under Matlab for single input single output (SISO) model predictive control (MPC) for any constrained optimization problem (trajectory tracking, processing time minimization...). The control structure is an adaptation of MPC with internal model control (IMC) structure. The algorithm was applie...

This paper focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian Processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GPs. This fact allows obtaining the confide...

This work presents a literature review of control methods, with an emphasis on the theory and applications of model predictive control (MPC) for heating, ventilation, and air conditioning (HVAC) systems. Several control methods used for HVAC control are identified from the literature review, and a brief survey of each method is presented. Next, the performance of MPC is compared with that of ot...

If you want to cite this report, please use the following reference instead: T.J.J. van den Boom and B. De Schutter, " Model predictive control for perturbed max-plus-linear systems, " Abstract Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class ...

This paper focuses on the developing a mechanistic model of the biopolymerization process and linking with the feedforward neural network model to obtain a hybrid model of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) PCL production using feed forward neural networks and control using model predictive control. Model predictive control (MPC) has ...

This paper deals with the first principle model based predictive control of the primary drying stage of a freeze drying of solutions in vials. In the proposed control approach, any online control problem concerned with a constrained optimization during the primary drying stage may be stated. It is solved using a special model predictive control framework where the model is used in the controlle...

In the real applications, the model predictive control MPC technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. In the literature, conditions for offset-free linear model predictive control are given for combined estimator for both the artificial disturbance and system state , steady-st...

Model predictive control (MPC) is a very popular controller design method in the process industry. One of the main advantages of MPC is that it can handle constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. Recently we have extended this framework to max-plus-linear discrete event systems. In this paper we further explore this topic. More specifically, we focus ...