723,402 research outputs found
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
The measurement and provision of precise and upto-date traffic-related key
performance indicators is a key element and crucial factor for intelligent
traffic controls systems in upcoming smart cities. The street network is
considered as a highly-dynamic Cyber Physical System (CPS) where measured
information forms the foundation for dynamic control methods aiming to optimize
the overall system state. Apart from global system parameters like traffic flow
and density, specific data such as velocity of individual vehicles as well as
vehicle type information can be leveraged for highly sophisticated traffic
control methods like dynamic type-specific lane assignments. Consequently,
solutions for acquiring these kinds of information are required and have to
comply with strict requirements ranging from accuracy over cost-efficiency to
privacy preservation. In this paper, we present a system for classifying
vehicles based on their radio-fingerprint. In contrast to other approaches, the
proposed system is able to provide real-time capable and precise vehicle
classification as well as cost-efficient installation and maintenance, privacy
preservation and weather independence. The system performance in terms of
accuracy and resource-efficiency is evaluated in the field using comprehensive
measurements. Using a machine learning based approach, the resulting success
ratio for classifying cars and trucks is above 99%
Learning Feedback Terms for Reactive Planning and Control
With the advancement of robotics, machine learning, and machine perception,
increasingly more robots will enter human environments to assist with daily
tasks. However, dynamically-changing human environments requires reactive
motion plans. Reactivity can be accomplished through replanning, e.g.
model-predictive control, or through a reactive feedback policy that modifies
on-going behavior in response to sensory events. In this paper, we investigate
how to use machine learning to add reactivity to a previously learned nominal
skilled behavior. We approach this by learning a reactive modification term for
movement plans represented by nonlinear differential equations. In particular,
we use dynamic movement primitives (DMPs) to represent a skill and a neural
network to learn a reactive policy from human demonstrations. We use the well
explored domain of obstacle avoidance for robot manipulation as a test bed. Our
approach demonstrates how a neural network can be combined with physical
insights to ensure robust behavior across different obstacle settings and
movement durations. Evaluations on an anthropomorphic robotic system
demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Fast Reinforcement Learning for Energy-Efficient Wireless Communications
We consider the problem of energy-efficient point-to-point transmission of
delay-sensitive data (e.g. multimedia data) over a fading channel. Existing
research on this topic utilizes either physical-layer centric solutions, namely
power-control and adaptive modulation and coding (AMC), or system-level
solutions based on dynamic power management (DPM); however, there is currently
no rigorous and unified framework for simultaneously utilizing both
physical-layer centric and system-level techniques to achieve the minimum
possible energy consumption, under delay constraints, in the presence of
stochastic and a priori unknown traffic and channel conditions. In this report,
we propose such a framework. We formulate the stochastic optimization problem
as a Markov decision process (MDP) and solve it online using reinforcement
learning. The advantages of the proposed online method are that (i) it does not
require a priori knowledge of the traffic arrival and channel statistics to
determine the jointly optimal power-control, AMC, and DPM policies; (ii) it
exploits partial information about the system so that less information needs to
be learned than when using conventional reinforcement learning algorithms; and
(iii) it obviates the need for action exploration, which severely limits the
adaptation speed and run-time performance of conventional reinforcement
learning algorithms. Our results show that the proposed learning algorithms can
converge up to two orders of magnitude faster than a state-of-the-art learning
algorithm for physical layer power-control and up to three orders of magnitude
faster than conventional reinforcement learning algorithms
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
Deep reinforcement learning (DRL) for fluidic pinball, three individually
rotating cylinders in the uniform flow arranged in an equilaterally triangular
configuration, can learn the efficient flow control strategies due to the
validity of self-learning and data-driven state estimation for complex fluid
dynamic problems. In this work, we present a DRL-based real-time feedback
strategy to control the hydrodynamic force on fluidic pinball, i.e., force
extremum and tracking, from cylinders' rotation. By adequately designing reward
functions and encoding historical observations, and after automatic learning of
thousands of iterations, the DRL-based control was shown to make reasonable and
valid control decisions in nonparametric control parameter space, which is
comparable to and even better than the optimal policy found through lengthy
brute-force searching. Subsequently, one of these results was analyzed by a
machine learning model that enabled us to shed light on the basis of
decision-making and physical mechanisms of the force tracking process. The
finding from this work can control hydrodynamic force on the operation of
fluidic pinball system and potentially pave the way for exploring efficient
active flow control strategies in other complex fluid dynamic problems
DESIGN, MODELING, AND CONTROL OF SOFT DYNAMIC SYSTEMS
Soft physical systems, be they elastic bodies, fluids, and compliant-bodied creatures, are ubiquitous in nature. Modeling and simulation of these systems with computer algorithms enable the creation of visually appealing animations, automated fabrication paradigms, and novel user interfaces and control mechanics to assist designers and engineers to develop new soft machines. This thesis develops computational methods to address the challenges emerged during the automation of the design, modeling, and control workflow supporting various soft dynamic systems. On the design/control side, we present a sketch-based design interface to enable non-expert users to design soft multicopters. Our system is endorsed by a data-driven algorithm to generate system identification and control policies given a novel shape prototype and rotor configurations. We show that our interactive system can automate the workflow of different soft multicopters\u27 design, simulation, and control with human designers involved in the loop. On the modeling side, we study the physical behaviors of fluidic systems from a local, collective perspective. We develop a prior-embedded graph network to uncover the local constraint relations underpinning a collective dynamic system such as particle fluid. We also proposed a simulation algorithm to model vortex dynamics with locally interacting Lagrangian elements. We demonstrate the efficacy of the two systems by learning, simulating and visualizing complicated dynamics of incompressible fluid
Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep Reinforcement Learning
With the development of communication technologies, connected autonomous
vehicles (CAVs) can share information with each other. We propose a novel
behavior planning method for CAVs to decide actions such as whether to change
lane or keep lane based on the observation and shared information from
neighbors, and to make sure that there exist corresponding control maneuvers
such as acceleration and steering angle to guarantee the safety of each
individual autonomous vehicle. We formulate this problem as a hybrid partially
observable Markov decision process (HPOMDP) to consider objectives such as
improving traffic flow efficiency and driving comfort and safety requirements.
The discrete state transition is determined by the proposed feedback deep
Q-learning algorithm using the feedback action from an underlying controller
based on control barrier functions. The feedback deep Q-learning algorithm we
design aims to solve the critical challenge of reinforcement learning (RL) in a
physical system: guaranteeing the safety of the system while the RL is
exploring the action space to increase the reward. We prove that our method
renders a forward invariant safe set for the continuous state physical dynamic
model of the system while the RL agent is learning. In experiments, our
behavior planning method can increase traffic flow and driving comfort compared
with the intelligent driving model (IDM). We also validate that our method
maintains safety during the learning process.Comment: conferenc
Automated design of complex dynamic systems
Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems
Modern Power System Dynamic Performance Improvement through Big Data Analysis
Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system
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