34,763 research outputs found
Switching control for incremental stabilization of nonlinear systems via contraction theory
In this paper we present a switching control strategy to incrementally
stabilize a class of nonlinear dynamical systems. Exploiting recent results on
contraction analysis of switched Filippov systems derived using regularization,
sufficient conditions are presented to prove incremental stability of the
closed-loop system. Furthermore, based on these sufficient conditions, a design
procedure is proposed to design a switched control action that is active only
where the open-loop system is not sufficiently incrementally stable in order to
reduce the required control effort. The design procedure to either locally or
globally incrementally stabilize a dynamical system is then illustrated by
means of a representative example.Comment: Accepted to ECC 201
An internal model approach to (optimal) frequency regulation in power grids with time-varying voltages
This paper studies the problem of frequency regulation in power grids under
unknown and possible time-varying load changes, while minimizing the generation
costs. We formulate this problem as an output agreement problem for
distribution networks and address it using incremental passivity and
distributed internal-model-based controllers. Incremental passivity enables a
systematic approach to study convergence to the steady state with zero
frequency deviation and to design the controller in the presence of
time-varying voltages, whereas the internal-model principle is applied to
tackle the uncertain nature of the loads.Comment: 16 pages. Abridged version appeared in the Proceedings of the 21st
International Symposium on Mathematical Theory of Networks and Systems, MTNS
2014, Groningen, the Netherlands. Submitted in December 201
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.Comment: International Conference on Robotics and Automation 201
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Theory of deferred action: Agent-based simulation model for designing complex adaptive systems
Deferred action is the axiom that agents act in emergent organisation to achieve predetermined goals. Enabling deferred action in designed artificial complex adaptive systems like business organisations and IS is problematical. Emergence is an intractable problem for designers because it cannot be predicted. We develop proof-of-concept, conceptual proto-agent model, of emergent organisation and emergent IS to understand better design principles to enable deferred action as a mechanism for coping with emergence in artefacts. We focus on understanding the effect of emergence when designing artificial complex adaptive systems by developing an exploratory proto-agent model and evaluate its suitability for implementation as agent-based simulation
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