Abstract: In this paper hybrid fuzzy model based predictive control (HFMBPC) is addressed, proposed and tested. The proposed hybrid fuzzy convolution model consists of a steady-state fuzzy model and a gain independent impulse response model. The proposed model is tested in model based predictive control of the concentration control in the chemical reactor, manipulating its flow rate. The paper ...

This paper presents a newly proposed Model Predictive (MPC) scheme featuring Particle Swarm Optimization (PSO) technique applied to nonlinear single area Automatic Generation Control (AGC). The proposed scheme formulates the MPC as an optimization problem and PSO is used to find its solution. Single area AGC model is used and it incorporates Generation Rate Constraint (GRC) nonlinearities and c...

Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multipleoutput plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, b...

A successful method for model predictive control of constrained linear systems uses a local linear control law that, in the presence of disturbances, maintains the disturbed trajectory of the controlled system in a tube whose centre is the nominal trajectory (generated ignoring the disturbance) and whose ‘cross-section’ is a robust, positively invariant set; robust exponential stability of an i...

When the difference between samples is measured using a Euclidean-embeddable dissimilarity function, observations and the associated variables can be displayed on a nonlinear biplot (Gower and Harding, 1988). Furthermore, a nonlinear biplot is predictive if information on variables is added in such a way that it allows the values of the variables to be estimated for points in the biplot. I will...

this paper presents a model predictive control (mpc) approach for production and inventory control systems. model predictive control previously has been successfully applied to supply chain problems; however most systems that have been proposed so far possess no information on future demand. the incorporation of a forecasting methodology in an mpc framework can promote the efficiency of control...

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

In this chapter, image-based visual servoing is addressed via nonlinear model predictive control. The visual servoing task is formulated into a nonlinear optimization problem in the image plane. The proposed approach, named visual predictive control, can easily and explicitly take into account 2D and 3D constraints. Furthermore, the image prediction over a finite prediction horizon plays a cruc...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...