Nonlinear Control Nonlinear Control and its Applications to UAVs and UGVs
Nonlinear Control of Genetic Regulatory Network
Adaptive Estimation with Output Feedback
This award for an REU site to the University of Texas
at San Antonio will provide undergraduate students research experiences in
nonlinear control and its applications in Unmanned Aerial Vehicles (UAVs) and
Unmanned Ground Vehicles (VGVs). The research projects will include trajectory
planning, obstacle avoidance, advanced nonlinear controls of UAVs and UGVs,
system simulation and performance analysis, validation of the algorithms with
real UAV/UGV platform. The REU program will serve to enrich the research
experiences of the participants by exposing the state-of-the-art developments
in UAVs/UGVs and by integrating research into real world applications. Also,
the program will enhance the undergraduate students' creative thinking and
independent problem solving capability and will motivate students to continue
graduate studies in science and engineering. Recruitment efforts will be
targeted to underrepresented minorities, with a special emphasis placed on the
Hispanic population. The research topics for the REU Site program are of interest
to our national security and defense. This study will benefit the US border
patrol team with long lasting autonomous inspection systems. It can also save
time and personnel while exploring hostile areas. For detailed information
about the REU site, please go to http://enginering.utsa.edu/~reu.
The
project presents research work on stability analysis and regulation of genetic
networks and pathways Genetic networks can be described by differential
equations with SUM logic which has been found in many natural systems. The
transcriptional rates of the genetic networks are important to the steady
state. Though we could not measure these transcriptional rates, biologists have
shown that they are alterable. A novel nonlinear adaptive controller is
proposed to drive the network to the desired steady level with real time
adjustment of these ratios. Asymptotic stability proof is conducted with Lyapunov argument and simulation results show the
effectiveness of such design.
Cardiovascular disease is the primary cause of death
in the U.S., and aging is a primary risk factor of cardiovascular disease.
Cardiovascular disease prevalence rises with aging. The primary discharge in
patients over 65 years of age is heart failure, and advanced age is a
demographic parameter that serves to predict heart failure mortality. Outcome
in older patients with acute coronary syndromes is poor, and aging is a major
risk factor for CV morbidity and mortality. Therefore, understanding the effect
of aging on cardiovascular function in the absence of underlying disease is of
great importance. Understanding of the natural aging process of cardiovascular
will provide a baseline for left ventricular remodeling post MI.
Computational analysis of
biochemical pathways and gene regulation network has been reported previously.
However, few studies have been carried out for LV remodeling post MI. The LV
remodeling process involves the deposition of extracellular matrix (ECM) to
form the infarct scar and prevent rupture.
Prolonged ECM deposition, however, leads to fibrosis, which increases
myocardial stiffness and further depresses LV function to culminate in
congestive heart failure. The objective
of this study was to measure extracellular matrix (ECM) gene levels in the LV
to identify candidate factors that are predictive of the LV remodeling post
MI. Coupling experiment and mathematical
modeling will allow us to determine which factors are most predictive of LV
remodeling post-MI and lead to significant clinical applications
Parameter estimation with
limited output feedback for chaotic system have been
investigated. System parameters are unknown but assumed to be bounded and stay
in a proper range that the nonlinear systems exhibit chaotic behavior. It's
shown that synchronization of states and estimation of the unknown system
parameters have been obtained. The proposed adaptation
law guarantees asymptotic convergence of the system. Simulation results also
show the effectiveness of the design and decoding of the message.