Friday 11 January 2019, 13.00 – 16.00, The Technical University of Denmark, Building 101, room S10
Supervisor: Associate Professor John Bagterp Jørgensen, DTU Compute
Examiners:
Associate Professor Jakob Kjøbsted Huusom, DTU Chemistry
Professor Matti Kalervo Vilkko, Tampere University of Technology, Finland
Managing Partner Guruprasath Muralidharan, Smarta-Opti Solutions, India
Moderator: Assistant Professor Dimitri Boiroux, DTU Compute
Summary:
Model based controller is one of the advanced control strategy that is currently
common and extensively recognized in industry and academic, famously known as
Model Predictive Control (MPC).
MPC is a controller that utilizes the identified model
of a system to predict its future behaviour, given a prediction horizon. The main idea
is to minimize the cost function and taking into account the constraints. Then the first
controller moves is implemented at a sampling instants over the control horizon, by
implementing only the first move the optimal feedback is achieved and then the
complete sequence will be repeated again, which is known as moving horizon
concept. Nowadays the applications of MPC are not limited to the process control
field, but also including other various fields.
This work described comprehensively an
outline
for MPC implementation for a linear system on a lab scale system in a simple
and constructive method.
Throughout this work, the
Modified Quadruple Tank System is utilized as an example
to assimilate the fundamental theory of Model Predictive Controller to an
exemplification of a multi-input-multi-ouput
system,
an illustration of the real-world
complex system applications which is widely used for education in demonstrating
advanced control strategies.
It is a simple process that is non-linear but demonstrates
complicated interactions between the manipulated and controlled variables.
The
system
consists of four identical tanks and two pumping systems.
Flows through the
pumps
can be controlled in order to achieve desired setpoints
of water levels in these
tank
s in occurrence of
some unknown measurement noise and stochastic
disturbance.
The thesis shows on the modeling part of the system, realization of a
linear discrete-time state space model, state estimation using Kalman filter and finally
demonstrates the application of MPC in a methodical mannered.
READ MORE about this thesis in DTU Orbit.