15,316 research outputs found
Adaptive performance optimization for large-scale traffic control systems
In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators
Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system
In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed
to take care of various contradictory objective functions for an Automatic
Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting
Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for
greater effectiveness, is used for the multi-objective optimization problem.
The Pareto fronts showing the trade-off between different design criteria are
obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is
done with respect to the standard PID controller to demonstrate the merits and
demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure
Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter
Plug and Play DC-DC Converters for Smart DC Nanogrids with Advanced Control Ancillary Services
This paper gives a general view of the control possibilities for dc-dc converters in dc nanogrids. A widely adopted control method is the droop control, which is able to achieve proportional load sharing among multiple sources and to stabilize the voltage of the dc distribution bus. Based on the droop control, several advanced control functions can be implemented. For example, power-based droop controllers allow dc-dc converters to operate with power flow control or droop control, whether the hosting nanogrid is operating connected to a strong upstream grid or it is operating autonomously (i.e., islanded). Converters can also be equipped with various supporting functions. Functions that are expected to play a crucial role in nanogrids that fully embrace the plug-and-play paradigm are those aiming at the monitoring and tuning of the key performance indices of the control loops. On-line stability monitoring tools respond to this need, by continuously providing estimates of the stability margins of the loops of interest; self- tuning can be eventually achieved on the basis of the obtained estimates. These control solutions can significantly enhance the operation and the plug-and-play feature of dc nanogrids, even with a variable number of hosted converters. Experimental results are reported to show the performance of the control approaches
Automatic LQR Tuning Based on Gaussian Process Global Optimization
This paper proposes an automatic controller tuning framework based on linear
optimal control combined with Bayesian optimization. With this framework, an
initial set of controller gains is automatically improved according to a
pre-defined performance objective evaluated from experimental data. The
underlying Bayesian optimization algorithm is Entropy Search, which represents
the latent objective as a Gaussian process and constructs an explicit belief
over the location of the objective minimum. This is used to maximize the
information gain from each experimental evaluation. Thus, this framework shall
yield improved controllers with fewer evaluations compared to alternative
approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is
used as the experimental demonstrator. Results of a two- and four-dimensional
tuning problems highlight the method's potential for automatic controller
tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference
on Robotics and Automation. Video demonstration of the experiments available
at https://am.is.tuebingen.mpg.de/publications/marco_icra_201
Combined simple cautious and robust control for parameter and disturbance bounded distributions
The qualities and drawbacks of two control methods to cope with process uncertainty are considered: cautious control which only uses statistics, and robust control which only uses the bounds of the process uncertainty. On the basis of results obtained for new simple methods for both, two new performance measures are introduced which use statistics as well as bounds of process parameters and disturbances, and therefore combine the qualities of cautious and robust control. These controllers are shown to outperform cautious and robust contro
The role of multiple marks in epigenetic silencing and the emergence of a stable bivalent chromatin state
We introduce and analyze a minimal model of epigenetic silencing in budding
yeast, built upon known biomolecular interactions in the system. Doing so, we
identify the epigenetic marks essential for the bistability of epigenetic
states. The model explicitly incorporates two key chromatin marks, namely H4K16
acetylation and H3K79 methylation, and explores whether the presence of
multiple marks lead to a qualitatively different systems behavior. We find that
having both modifications is important for the robustness of epigenetic
silencing. Besides the silenced and transcriptionally active fate of chromatin,
our model leads to a novel state with bivalent (i.e., both active and
silencing) marks under certain perturbations (knock-out mutations, inhibition
or enhancement of enzymatic activity). The bivalent state appears under several
perturbations and is shown to result in patchy silencing. We also show that the
titration effect, owing to a limited supply of silencing proteins, can result
in counter-intuitive responses. The design principles of the silencing system
is systematically investigated and disparate experimental observations are
assessed within a single theoretical framework. Specifically, we discuss the
behavior of Sir protein recruitment, spreading and stability of silenced
regions in commonly-studied mutants (e.g., sas2, dot1) illuminating the
controversial role of Dot1 in the systems biology of yeast silencing.Comment: Supplementary Material, 14 page
Automatic controls and regulators: A compilation
Devices, methods, and techniques for control and regulation of the mechanical/physical functions involved in implementing the space program are discussed. Section one deals with automatic controls considered to be, essentially, start-stop operations or those holding the activity in a desired constraint. Devices that may be used to regulate activities within desired ranges or subject them to predetermined changes are dealt with in section two
Phase resetting reveals network dynamics underlying a bacterial cell cycle
Genomic and proteomic methods yield networks of biological regulatory
interactions but do not provide direct insight into how those interactions are
organized into functional modules, or how information flows from one module to
another. In this work we introduce an approach that provides this complementary
information and apply it to the bacterium Caulobacter crescentus, a paradigm
for cell-cycle control. Operationally, we use an inducible promoter to express
the essential transcriptional regulatory gene ctrA in a periodic, pulsed
fashion. This chemical perturbation causes the population of cells to divide
synchronously, and we use the resulting advance or delay of the division times
of single cells to construct a phase resetting curve. We find that delay is
strongly favored over advance. This finding is surprising since it does not
follow from the temporal expression profile of CtrA and, in turn, simulations
of existing network models. We propose a phenomenological model that suggests
that the cell-cycle network comprises two distinct functional modules that
oscillate autonomously and couple in a highly asymmetric fashion. These
features collectively provide a new mechanism for tight temporal control of the
cell cycle in C. crescentus. We discuss how the procedure can serve as the
basis for a general approach for probing network dynamics, which we term
chemical perturbation spectroscopy (CPS)
- …