11 research outputs found
Event-triggered Learning for Resource-efficient Networked Control
Common event-triggered state estimation (ETSE) algorithms save communication
in networked control systems by predicting agents' behavior, and transmitting
updates only when the predictions deviate significantly. The effectiveness in
reducing communication thus heavily depends on the quality of the dynamics
models used to predict the agents' states or measurements. Event-triggered
learning is proposed herein as a novel concept to further reduce communication:
whenever poor communication performance is detected, an identification
experiment is triggered and an improved prediction model learned from data.
Effective learning triggers are obtained by comparing the actual communication
rate with the one that is expected based on the current model. By analyzing
statistical properties of the inter-communication times and leveraging powerful
convergence results, the proposed trigger is proven to limit learning
experiments to the necessary instants. Numerical and physical experiments
demonstrate that event-triggered learning improves robustness toward changing
environments and yields lower communication rates than common ETSE.Comment: 7 pages, 4 figures, to appear in the 2018 American Control Conference
(ACC
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
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
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
Linear Regression over Networks with Communication Guarantees
A key functionality of emerging connected autonomous systems such as smart
cities, smart transportation systems, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in connected autonomous
systems, data transfer takes place over communication networks with often
limited resources. This paper examines algorithms for communication-efficient
learning for linear regression tasks by exploiting the informativeness of the
data. The developed algorithms enable a tradeoff between communication and
learning with theoretical performance guarantees and efficient practical
implementations.Comment: Accepted at 3rd Annual Learning for Dynamics & Control Conference
(L4DC) 2021. arXiv admin note: substantial text overlap with arXiv:2101.1000
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Communication load is a limiting factor in many real-time systems.
Event-triggered state estimation and event-triggered learning methods reduce
network communication by sending information only when it cannot be adequately
predicted based on previously transmitted data. This paper proposes an
event-triggered learning approach for nonlinear discrete-time systems with
cyclic excitation. The method automatically recognizes cyclic patterns in data
- even when they change repeatedly - and reduces communication load whenever
the current data can be accurately predicted from previous cycles. Nonetheless,
a bounded error between original and received signal is guaranteed. The cyclic
excitation model, which is used for predictions, is updated hierarchically,
i.e., a full model update is only performed if updating a small number of model
parameters is not sufficient. A nonparametric statistical test enforces that
model updates happen only if the cyclic excitation changed with high
probability. The effectiveness of the proposed methods is demonstrated using
the application example of wireless real-time pitch angle measurements of a
human foot in a feedback-controlled neuroprosthesis. The experimental results
show that communication load can be reduced by 70 % while the root-mean-square
error between measured and received angle is less than 1{\deg}.Comment: 6 pages and 6 figures; to appear in IEEE Control Systems Letter
Event-triggered Learning for Linear Quadratic Control
When models are inaccurate, the performance of model-based control will
degrade. For linear quadratic control, an event-triggered learning framework is
proposed that automatically detects inaccurate models and triggers the learning
of a new process model when needed. This is achieved by analyzing the
probability distribution of the linear quadratic cost and designing a learning
trigger that leverages Chernoff bounds. In particular, whenever empirically
observed cost signals are located outside the derived confidence intervals, we
can provably guarantee that this is with high probability due to a model
mismatch. With the aid of numerical and hardware experiments, we demonstrate
that the proposed bounds are tight and that the event-triggered learning
algorithm effectively distinguishes between inaccurate models and probabilistic
effects such as process noise. Thus, a structured approach is obtained that
decides when model learning is beneficial.Comment: 13 pages, 8 figures, accepted for publication in IEEE Transactions on
Automatic Contro