Research Interests
Our research interests are in the general areas of
process control, dynamics and optimization, computational modeling and simulation of
complex systems, and applied mathematics. The central objective of our research is
the development of novel methods for the systematic and rigorous solution of complex
process control and systems engineering problems. Our research team
is part of the UCLA Center for Systems,
Dynamics and Control and of the Process Systems Engineering Group. The
following research directions are currently pursued: Nonlinear process control and monitoring
Chemical processes are inherently nonlinear and cannot be effectively
controlled and monitored with conventional control and estimation schemes which are
developed on the basis of linear or linearized process models. To enhance our ability to
operate chemical processes, our research focuses on: a) the development of a rigorous and
practical framework for nonlinear model-based control of chemical processes
(including the recently-proposed hybrid predictive control technique) that
explicitly accounts for the presence of uncertainty and time-scale multiplicity in the
process model, and constraints and time-delays in the control actuators and measurement
sensors, b) the development of nonlinear state and parameter estimation algorithms for
process monitoring, c) the development of accurate nonlinear process models from plant
data and fundamental process understanding, and d) the development of
integrated fault-detection and fault-tolerant control approaches for
nonlinear processes. The theoretical studies are coupled with
applications to complex chemical processes.
Analysis and control of nonlinear hybrid process
systems
The objective of this research is to develop a comprehensive framework for
nonlinear model-based feedback control of multivariable hybrid nonlinear processes (i.e.,
processes with combined continuous dynamics and discrete events). Both lumped and
spatially-distributed hybrid systems are studied. Lyapunov theory is employed to produce
novel analytical nonlinear feedback
controller designs and switching laws that orchestrate the transition between the process
continuous modes. The new control strategies deal explicitly with control actuator
constraints and model uncertainty and enforce the desired stability, performance and
robustness specifications in the hybrid closed-loop system. The theoretical results
are used to develop integrated supervisory/feedback control systems for processes with
combined continuous/discrete dynamics, nonlinearities, uncertainty and constraints.
Model reduction, optimization and control of
nonlinear distributed parameter systems and multiscale systems
Distributed parameter systems (DPS) like integro-differential equations and
partial differential equations arise naturally in the mathematical modeling of particulate
and transport-reaction processes. The main feature of DPS is that they are
characterized by infinite dimensional dynamic behavior. Therefore, it is impossible
to perform dynamical analysis, optimization and design, and synthesize
practically-implementable controllers for particulate and transport-reaction processes
based on full distributed parameter models. The objectives of this research are:
a) the development of model reduction methods for the derivation of low-order
systems that accurately reproduce the solution and dynamics of a DPS, b) the development
of optimization algorithms and the synthesis of nonlinear controllers, which are
guaranteed to work for the DPS, based on the low-order approximations, and c) the
development of a general framework for integrated optimal design and control of DPS.
The theoretical results are applied to particulate processes like continuous and
batch crystallization and aerosol production for particle size distribution control, as
well as to transport-reaction processes used in advanced materials and semiconductor
processing including crystal growth, rapid thermal processing, and plasma-assisted
chemical vapor deposition and etching. In addition to the above activities, current
research in this direction also focuses on the development of order reduction and control
methods for hybrid (continuous/discrete) and multiscale (deterministic/stochastic)
distributed systems.
Modeling and control of nanoparticle synthesis and processing
While some recent efforts have been done on control of size distribution in
aerosol reactors, the problem of developing an integrated approach to real-time
monitoring and control of nanoparticle synthesis in aerosol processes remains largely
unresolved. In addition to shaping particle size distribution, feedback control may be
used to achieve the desired particle morphology, composition and degree of agglomeration
that influence subsequent processing and product properties. Furthermore, real-time
control of nanoparticle processing systems, for example thermal spray processing of
nanostructured coatings using nanosized powders, has not been attempted. Recent advances
in on-line particle size distribution measurement including laser absorption scattering
and probe sampling techniques provide the means for achieving real-time nonlinear feedback
control of nanoparticle synthesis and processing. We develop a systematic multiscale
approach to real-time control of processes involved in the synthesis and processing of
nanoparticles. We address the development of low-order approximations of multiscale models
linking macroscopic scale (e.g., thermal spray process) and microscopic scale (e.g.,
evolution of coating microstructure) and the integration of models, measurements and
control theory to develop real-time feedback control systems.
Feedback control of fluid flows
The problem of using active feedback control to stabilize transitional and
turbulent flows is a fundamental one whose solution may have a very significant impact on
the design and operation of airplanes, automobiles, and underwater vehicles. We work on
the development of a rigorous and practical framework for nonlinear control of
transitional and turbulent fluid flows based on accurate low-order approximations of the
Navier-Stokes equations that describe the flow. Our research systematically addresses the
synthesis of the control configuration and controller, as well as key practical
implementation issues like the selection of the type and location of control actuators and
measurement sensors. The theoretical results are applied to transitional and turbulent
channel and boundary layer flows for drag reduction.
Water
systems modeling and control
In this direction, our
research (in collaboration with Professor Y. Cohen) focuses on the
modeling, analysis and control of water processing and distribution
systems with particular emphasis on real-time fault diagnosis and
control of water desalination plants. This research covers both
development of user-friendly software tools for the practical
implementation of UCLA-developed algorithms and applications to
experimental systems and pilot plants.
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