409,413 research outputs found
Controlling Complex Systems Dynamics without Prior Model
International audienceControlling complex systems imposes to deal with high dynamics, non-linearity and multiple interdependencies. To handle these difÂżculties we can either build analytic models of the process to control, or enable the controller to learn how the process behaves. Adaptive Multi-Agent Systems (AMAS) are able to learn and adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. The problem of control leads to a speciÂżc architecture presented in this paper
Superconducting circuit optomechanics in topological lattices
Cavity optomechanics enables controlling mechanical motion via radiation
pressure interaction, and has contributed to the quantum control of engineered
mechanical systems ranging from kg scale LIGO mirrors to nano-mechanical
systems, enabling ground-state preparation, entanglement, squeezing of
mechanical objects, position measurements at the standard quantum limit,
non-reciprocal photon transport, and quantum transduction. Yet, nearly all
prior schemes have employed single- or few-mode op-tomechanical systems. In
contrast, novel dynamics and applications are expected when utilizing
optomechanical arrays and lattices, which enable to synthesize non-trivial band
structures, and have been actively studied in the field of circuit QED.
Superconducting microwave optomechanical circuits are a promising platform to
implement such lattices, but have been compounded by strict scaling
limitations. Here, we overcome this challenge and realize superconducting
circuit optomechanical lattices. We demonstrate non-trivial topological
microwave modes in 1D optomechanical chains realizing the canonical
Su-Schrieffer-Heeger (SSH) model. Furthermore, we realize the strained graphene
model in a 2D optomechanical honeycomb lattice. Exploiting the embedded
optomechanical interaction, we show that it is possible to directly measure the
mode functions of the bulk modes, as well as the topologically protected edge
states, without using any local probe or inducing perturbation. This enables us
to reconstruct the full underlying lattice Hamiltonian. Such optomechanical
lattices, accompanied by the measurement techniques introduced, offers an
avenue to explore out of equilibrium physics in optomechanical lattices such as
collective, quantum many-body, and quench dynamics, topological properties and
more broadly, emergent nonlinear dynamics in complex optomechanical systems
with a large number of degrees of freedoms.Comment: Updated version with a comprehensive discussion on strained graphene
mode
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Enabling controlling complex networks with local topological information
Complex networks characterize the nature of internal/external interactions in real-world systems
including social, economic, biological, ecological, and technological networks. Two issues keep as
obstacles to fulflling control of large-scale networks: structural controllability which describes the
ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a
suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost
for driving the network to a predefned state with a given number of control inputs. For large complex
networks without global information of network topology, both problems remain essentially open.
Here we combine graph theory and control theory for tackling the two problems in one go, using only
local network topology information. For the structural controllability problem, a distributed local-game
matching method is proposed, where every node plays a simple Bayesian game with local information
and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity.
Starring from any structural controllability solution, a minimizing longest control path method can
efciently reach a good solution for the optimal control in large networks. Our results provide solutions
for distributed complex network control and demonstrate a way to link the structural controllability and
optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio
Control energy of complex networks towards distinct mixture states
Controlling complex networked systems is a real-world puzzle that remains largely unsolved. Despite recent progress in understanding the structural characteristics of network control energy, target state and system dynamics have not been explored. We examine how varying the final state mixture affects the control energy of canonical and conformity-incorporated dynamical systems. We find that the control energy required to drive a network to an identical final state is lower than that required to arrive a non-identical final state. We also demonstrate that it is easier to achieve full control in a conformity-based dynamical network. Finally we determine the optimal control strategy in terms of the network hierarchical structure. Our work offers a realistic understanding of the control energy within the final state mixture and sheds light on controlling complex systems.This work was funded by The National Natural Science Foundation of China (Grant Nos. 61763013, 61703159, 61403421), The Natural Science Foundation of Jiangxi Province (No. 20171BAB212017), The Measurement and Control of Aircraft at Sea Laboratory (No. FOM2016OF010), and China Scholarship Council (201708360048). The Boston University Center for Polymer Studies is supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE-1213217, and by DTRA Grant HDTRA1-14-1-0017. (61763013 - National Natural Science Foundation of China; 61703159 - National Natural Science Foundation of China; 61403421 - National Natural Science Foundation of China; 20171BAB212017 - Natural Science Foundation of Jiangxi Province; FOM2016OF010 - Measurement and Control of Aircraft at Sea Laboratory; 201708360048 - China Scholarship Council; PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - DTRA)Published versio
Optimal treatment allocations in space and time for on-line control of an emerging infectious disease
A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulationâoptimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America
Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms
open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarmâs inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarmâs emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)
Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia
Collaborative crowdsourcing has become a popular approach to organizing work
across the globe. Being global also means being vulnerable to shocks --
unforeseen events that disrupt crowds -- that originate from any country. In
this study, we examine changes in collaborative behavior of editors of Chinese
Wikipedia that arise due to the 2005 government censor- ship in mainland China.
Using the exogenous variation in the fraction of editors blocked across
different articles due to the censorship, we examine the impact of reduction in
group size, which we denote as the shock level, on three collaborative behavior
measures: volume of activity, centralization, and conflict. We find that
activity and conflict drop on articles that face a shock, whereas
centralization increases. The impact of a shock on activity increases with
shock level, whereas the impact on centralization and conflict is higher for
moderate shock levels than for very small or very high shock levels. These
findings provide support for threat rigidity theory -- originally introduced in
the organizational theory literature -- in the context of large-scale
collaborative crowds
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