70,616 research outputs found
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
Distributed control of reactive power flow in a radial distribution circuit with high photovoltaic penetration
We show how distributed control of reactive power can serve to regulate
voltage and minimize resistive losses in a distribution circuit that includes a
significant level of photovoltaic (PV) generation. To demonstrate the
technique, we consider a radial distribution circuit with a single branch
consisting of sequentially-arranged residential-scale loads that consume both
real and reactive power. In parallel, some loads also have PV generation
capability. We postulate that the inverters associated with each PV system are
also capable of limited reactive power generation or consumption, and we seek
to find the optimal dispatch of each inverter's reactive power to both maintain
the voltage within an acceptable range and minimize the resistive losses over
the entire circuit. We assume the complex impedance of the distribution circuit
links and the instantaneous load and PV generation at each load are known. We
compare the results of the optimal dispatch with a suboptimal local scheme that
does not require any communication. On our model distribution circuit, we
illustrate the feasibility of high levels of PV penetration and a significant
(20% or higher) reduction in losses.Comment: 6 pages, 5 figures
Decentralized Implementation of Centralized Controllers for Interconnected Systems
Given a centralized controller associated with a linear time-invariant interconnected system, this paper is concerned with designing a parameterized decentralized
controller such that the state and input of
the system under the obtained decentralized controller can become arbitrarily close to those of the system under the given centralized controller, by tuning the controller's parameters. To this end, a two-level decentralized controller is designed, where the upper level captures the dynamics of the centralized closed-loop system, and the lower level is an observed-based sub-controller designed based on the new notion of structural initial value
observability. The proposed method can decentralize every generic centralized controller, provided the interconnected system satisfies very mild conditions. The efficacy of this work is elucidated by some numerical examples
Gather-and-broadcast frequency control in power systems
We propose a novel frequency control approach in between centralized and
distributed architectures, that is a continuous-time feedback control version
of the dual decomposition optimization method. Specifically, a convex
combination of the frequency measurements is centrally aggregated, followed by
an integral control and a broadcast signal, which is then optimally allocated
at local generation units. We show that our gather-and-broadcast control
architecture comprises many previously proposed strategies as special cases. We
prove local asymptotic stability of the closed-loop equilibria of the
considered power system model, which is a nonlinear differential-algebraic
system that includes traditional generators, frequency-responsive devices, as
well as passive loads, where the sources are already equipped with primary
droop control. Our feedback control is designed such that the closed-loop
equilibria of the power system solve the optimal economic dispatch problem
Options for Control of Reactive Power by Distributed Photovoltaic Generators
High penetration levels of distributed photovoltaic(PV) generation on an
electrical distribution circuit present several challenges and opportunities
for distribution utilities. Rapidly varying irradiance conditions may cause
voltage sags and swells that cannot be compensated by slowly responding utility
equipment resulting in a degradation of power quality. Although not permitted
under current standards for interconnection of distributed generation,
fast-reacting, VAR-capable PV inverters may provide the necessary reactive
power injection or consumption to maintain voltage regulation under difficult
transient conditions. As side benefit, the control of reactive power injection
at each PV inverter provides an opportunity and a new tool for distribution
utilities to optimize the performance of distribution circuits, e.g. by
minimizing thermal losses. We discuss and compare via simulation various design
options for control systems to manage the reactive power generated by these
inverters. An important design decision that weighs on the speed and quality of
communication required is whether the control should be centralized or
distributed (i.e. local). In general, we find that local control schemes are
capable for maintaining voltage within acceptable bounds. We consider the
benefits of choosing different local variables on which to control and how the
control system can be continuously tuned between robust voltage control,
suitable for daytime operation when circuit conditions can change rapidly, and
loss minimization better suited for nighttime operation.Comment: 8 pages, 8 figure
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