58,328 research outputs found
Quantum control theory and applications: A survey
This paper presents a survey on quantum control theory and applications from
a control systems perspective. Some of the basic concepts and main developments
(including open-loop control and closed-loop control) in quantum control theory
are reviewed. In the area of open-loop quantum control, the paper surveys the
notion of controllability for quantum systems and presents several control
design strategies including optimal control, Lyapunov-based methodologies,
variable structure control and quantum incoherent control. In the area of
closed-loop quantum control, the paper reviews closed-loop learning control and
several important issues related to quantum feedback control including quantum
filtering, feedback stabilization, LQG control and robust quantum control.Comment: 38 pages, invited survey paper from a control systems perspective,
some references are added, published versio
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
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
A Theory of Cheap Control in Embodied Systems
We present a framework for designing cheap control architectures for embodied
agents. Our derivation is guided by the classical problem of universal
approximation, whereby we explore the possibility of exploiting the agent's
embodiment for a new and more efficient universal approximation of behaviors
generated by sensorimotor control. This embodied universal approximation is
compared with the classical non-embodied universal approximation. To exemplify
our approach, we present a detailed quantitative case study for policy models
defined in terms of conditional restricted Boltzmann machines. In contrast to
non-embodied universal approximation, which requires an exponential number of
parameters, in the embodied setting we are able to generate all possible
behaviors with a drastically smaller model, thus obtaining cheap universal
approximation. We test and corroborate the theory experimentally with a
six-legged walking machine. The experiments show that the sufficient controller
complexity predicted by our theory is tight, which means that the theory has
direct practical implications. Keywords: cheap design, embodiment, sensorimotor
loop, universal approximation, conditional restricted Boltzmann machineComment: 27 pages, 10 figure
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