In this work, a computationally
efficient nonlinear model-based
control (NMBC) strategy is developed for a trajectory-tracking problem
in an acrylamide polymerization batch reactor. The performance of
NMBC is compared with that of nonlinear model predictive control (NMPC).
To estimate the reaction states, a nonlinear state estimator, an unscented
Kalman filter (UKF), is employed. Both algorithms are implemented
experimentally to track a time-varying temperature profile for an
acrylamide polymerization reaction in a lab-scale polymerization reactor.
It is shown that in the presence of state estimators the NMBC performs
significantly better than the NMPC algorithm in real time for the
batch reactor control problem.
Batch process plays
a very crucial and important role in process
industries. The increased operational flexibility and trend toward
high-quality, low-volume chemical production has put more emphasis
on batch processing. In this work, nonlinearities associated with
the batch reactor process have been studied. ARX and NARX models have
been identified using open-loop data obtained from the pilot plant
batch reactor. The performance of the batch reactor with conventional
linear controllers results in aggressive manipulated variable action
and larger energy consumption due to its inherent nonlinearity. This
issue has been addressed in the proposed work by identifying the nonlinear
model and designing a nonlinear model predictive controller for a
pilot plant batch reactor. The implementation of the proposed method
has resulted in smooth response of the manipulated variable as well
as reactor temperature on both simulation and real-time experimentation.
Batch reactors are
large vessels in which chemical reactions take
place. They are mostly found to be used in process control industries
for processes such as reactant mixing, waste treatment of leather
byproducts, and liquid extraction. Modeling and controlling of these
systems are complex due to their highly nonlinear nature. The Wiener
neural network (WNN) is employed in this work to predict and track
the temperature profile of a batch reactor successfully. WNN is different
from artificial neural networks in various aspects, mainly its structure.
The brief methodology that was deployed to complete this work consisted
of two parts. The first part is modeling the WNN-based batch reactor
using the provided input–output data set. The input is feed
given to the reactor, and the reactor temperature needs to be maintained
in line with the optimal profile. The objective in this part is to
train the neural network to efficiently track the nonlinear temperature
profile that is provided from the data set. The second part is designing
a generalized predictive controller (GPC) using the data obtained
from modeling the reactor to successfully track any arbitrary temperature
profile. Therefore, this work presents the experimental modeling of
a batch reactor and validation of a WNN-based GPC for temperature
profile tracking.
This paper presents the novelty on a nonlinear proportional
integral
derivative (NPID) controller developed from the gain values obtained
using the Lyapunov-based nonlinear model predictive controller (LyNMPC).
The tuning parameters of the proposed controller are taken from the
dynamics of the nonlinear system, and these parmeters are dynamic
with their value varying according to the error in the system. In
this article, the authors have considered two highly nonlinear systems,
namely, batch polymerization reactor and quadrotor unmanned aerial
vehicle systems. The nonlinear mathematical modeling of the batch
reactor as well as the quadrotor system considered from the past literature
of authors. The acrylamide polymerization reaction under consideration
is an exothermic reaction, thereby making the temperature profile
tracking and control a challenging task. The primary aim of this article
is to develop the NPID controller based on the LyNMPC algorithm and
to validate the NPID on a batch reactor bench-scale plant and on an
hardware-in-the-loop platform for the quadrotor hardware. A comparative
study of trajectory tracking and control capabilities of LyNMPC on
derived non-linear models of the batch reactor and quadrotor system
is presented. The system mathematical models are obtained with the
help of the first-principle energy balance equation for the batch
reactor and with the nonlinear dynamics of the quadrotor which is
derived based on Newton–Euler formulations. With LyNMPC, the
stability of the nonlinear systems can be improved because the error
sensitivity is considered in the cost function.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.