Moving horizon predictive control of nonlinear systems

OData support
Supervisor:
Dr. Lantos Béla
Department of Control Engineering and Information Technology

In this thesis I have examined the model predictive control (MPC) of nonlinear

systems. I reviewed the applications of MPC technology. I also presented the

mathematical background of the MPC method, as required by the two case studies;

a Newell and Lee evaporator and a robotic arm of two degrees of freedom. I

refer to the literature, where the detailed deductions can be found. I have developed

from literary sources the detailed dynamic models of the two cases. I got

to know the enviroment of MATLAB Model Predictive Control Toolbox within

an evaporator regulatory task. As I used a control algorithm for linear systems,

I had to linearize it at given workpoints.

Then instead of working in a Model Predictive Control Toolbox enviroment, I

solved the contol problem with the help of quadprog function of the Optimization

Toolbox. Applying the moving horizon method and keeping the constraints, it led

to a quadratic problem. I worked out the control of a robotic arm in two degrees

of freedom on a square path using the Optimization Toolbox. Both linearizations

of the nonlinear systems are described in detail. The simulation results are presented

in diagrams. I also examined the possibility of exchanging the function of Optimization Toolbox

to embedded Simulink function.

Downloads

Please sign in to download the files of this thesis.