46,354 research outputs found
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control
We present an algorithm for controlling and scheduling multiple linear
time-invariant processes on a shared bandwidth limited communication network
using adaptive sampling intervals. The controller is centralized and computes
at every sampling instant not only the new control command for a process, but
also decides the time interval to wait until taking the next sample. The
approach relies on model predictive control ideas, where the cost function
penalizes the state and control effort as well as the time interval until the
next sample is taken. The latter is introduced in order to generate an adaptive
sampling scheme for the overall system such that the sampling time increases as
the norm of the system state goes to zero. The paper presents a method for
synthesizing such a predictive controller and gives explicit sufficient
conditions for when it is stabilizing. Further explicit conditions are given
which guarantee conflict free transmissions on the network. It is shown that
the optimization problem may be solved off-line and that the controller can be
implemented as a lookup table of state feedback gains. Simulation studies which
compare the proposed algorithm to periodic sampling illustrate potential
performance gains.Comment: Accepted for publication in IEEE Transactions on Control Systems
Technolog
Event-triggered Learning
The efficient exchange of information is an essential aspect of intelligent
collective behavior. Event-triggered control and estimation achieve some
efficiency by replacing continuous data exchange between agents with
intermittent, or event-triggered communication. Typically, model-based
predictions are used at times of no data transmission, and updates are sent
only when the prediction error grows too large. The effectiveness in reducing
communication thus strongly depends on the quality of the prediction model. In
this article, we propose event-triggered learning as a novel concept to reduce
communication even further and to also adapt to changing dynamics. By
monitoring the actual communication rate and comparing it to the one that is
induced by the model, we detect a mismatch between model and reality and
trigger model learning when needed. Specifically, for linear Gaussian dynamics,
we derive different classes of learning triggers solely based on a statistical
analysis of inter-communication times and formally prove their effectiveness
with the aid of concentration inequalities
Event-triggered Pulse Control with Model Learning (if Necessary)
In networked control systems, communication is a shared and therefore scarce
resource. Event-triggered control (ETC) can achieve high performance control
with a significantly reduced amount of samples compared to classical, periodic
control schemes. However, ETC methods usually rely on the availability of an
accurate dynamics model, which is oftentimes not readily available. In this
paper, we propose a novel event-triggered pulse control strategy that learns
dynamics models if necessary. In addition to adapting to changing dynamics, the
method also represents a suitable replacement for the integral part typically
used in periodic control.Comment: Accepted final version to appear in: Proc. of the American Control
Conference, 201
Decentralized event-triggered control over wireless sensor/actuator networks
In recent years we have witnessed a move of the major industrial automation
providers into the wireless domain. While most of these companies already offer
wireless products for measurement and monitoring purposes, the ultimate goal is
to be able to close feedback loops over wireless networks interconnecting
sensors, computation devices, and actuators. In this paper we present a
decentralized event-triggered implementation, over sensor/actuator networks, of
centralized nonlinear controllers. Event-triggered control has been recently
proposed as an alternative to the more traditional periodic execution of
control tasks. In a typical event-triggered implementation, the control signals
are kept constant until the violation of a condition on the state of the plant
triggers the re-computation of the control signals. The possibility of reducing
the number of re-computations, and thus of transmissions, while guaranteeing
desired levels of performance makes event-triggered control very appealing in
the context of sensor/actuator networks. In these systems the communication
network is a shared resource and event-triggered implementations of control
laws offer a flexible way to reduce network utilization. Moreover reducing the
number of times that a feedback control law is executed implies a reduction in
transmissions and thus a reduction in energy expenditures of battery powered
wireless sensor nodes.Comment: 13 pages, 3 figures, journal submissio
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