The practicality of model predictive control (MPC) is partially limited by the ability to solve optimization problems in real time. This requirement limits the viability of MPC as a control strategy for large scale processes. One strategy for improving the computational performance is to formulate MPC using a linear program. While the linear programming formulation seems appealing from a numeri...

This paper is focused on a comprehensive study of neural network (NN) model based predictive control (MPC), as an operation strategy for a fed-batch sugar crystallizer. The process is divided into four subsequent control loops and for each of them an individual NN-based MPC is designed. The operation is tested for a number of scenarios and is compared with alternative (linear and batch nonlinea...

This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies a...

Control of complex nonlinear systems brings challenges in the controller design. One of methods how to cope with this challenge is the usage of advanced optimization methods. This work presents application of self-organizing migrating algorithm (SOMA) in control of the semi-batch reactor. The reactor is used in chromium recycling process in leather industry. Because of the complexity of this se...

Semicontinuous distillation is a process intensification technique for purification of multicomponent mixtures. The system is control-driven and thus the control structure and its tuning parameters have crucial importance in the operation and the economics of the process. In this study, for the first time, implementation of the model predictive control (MPC) on a semicontinuous process is studi...

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 of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In our previous work we have considered MPC for the time-invariant case. In this paper we con...

This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we dev...

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Previously, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In this paper we consider the stability of MPC for these max-plus linear (MPL) systems, and...

In this paper, a coordinated-distributed model predictive control (MPC) scheme is presented for large-scale discrete-time linear process systems. Coordinated-distributed MPC control aims at enhancing the performance of fully decentralized MPC controllers by achieving the plant-wide optimal operations. The ‘price-driven’ decomposition-coordination method is used to adjust the operations of the i...

We present CODEV, a Matlab-based tool for verifying systems employing Model Predictive Control (MPC). The MPC solution is computed offline and modeled together with the physical system as a hybrid automaton, whose continuous dynamics may be nonlinear with a control solution that remains affine. While MPC is a widely used synthesis technique for constrained and optimal control in industry, our t...