149,864 research outputs found
Computing an Optimal Control Policy for an Energy Storage
We introduce StoDynProg, a small library created to solve Optimal Control
problems arising in the management of Renewable Power Sources, in particular
when coupled with an Energy Storage System. The library implements generic
Stochastic Dynamic Programming (SDP) numerical methods which can solve a large
class of Dynamic Optimization problems. We demonstrate the library capabilities
with a prototype problem: smoothing the power of an Ocean Wave Energy
Converter. First we use time series analysis to derive a stochastic Markovian
model of this system since it is required by Dynamic Programming. Then, we
briefly describe the "policy iteration" algorithm we have implemented and the
numerical tools being used. We show how the API design of the library is
generic enough to address Dynamic Optimization problems outside the field of
Energy Management. Finally, we solve the power smoothing problem and compare
the optimal control with a simpler heuristic control.Comment: Part of the Proceedings of the 6th European Conference on Python in
Science (EuroSciPy 2013), Pierre de Buyl and Nelle Varoquaux editors, (2014
Distributed Control with Low-Rank Coordination
A common approach to distributed control design is to impose sparsity
constraints on the controller structure. Such constraints, however, may greatly
complicate the control design procedure. This paper puts forward an alternative
structure, which is not sparse yet might nevertheless be well suited for
distributed control purposes. The structure appears as the optimal solution to
a class of coordination problems arising in multi-agent applications. The
controller comprises a diagonal (decentralized) part, complemented by a
rank-one coordination term. Although this term relies on information about all
subsystems, its implementation only requires a simple averaging operation
Decision rules and information provision: monitoring versus manipulation
The paper focuses on the organization of institutions designed to
resolve disputes between two parties, when some information is not
veri…able and decision makers may have vested preferences. It shows
that the choice of how much discretional power to grant to the decision
maker and who provides the information are intrinsically related. Direct
involvement of the interested parties in the supply of information
enhances monitoring over the decision maker, although at the cost of
higher manipulation. Thus, it is desirable when the decision maker is
granted high discretion. On the contrary, when the decision maker has
limited discretional power, information provision is better assigned to
an agent with no direct stake. The analysis helps to rationalize some
organizational arrangements that are commonly observed in the context
of judicial and antitrust decision-makin
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
Optimal control for unitary preparation of many-body states: application to Luttinger liquids
Many-body ground states can be prepared via unitary evolution in cold atomic
systems. Given the initial state and a fixed time for the evolution, how close
can we get to a desired ground state if we can tune the Hamiltonian in time?
Here we study this optimal control problem focusing on Luttinger liquids with
tunable interactions. We show that the optimal protocol can be obtained by
simulated annealing. We find that the optimal interaction strength of the
Luttinger liquid can have a nonmonotonic time dependence. Moreover, the system
exhibits a marked transition when the ratio of the preparation time to
the system size exceeds a critical value. In this regime, the optimal protocols
can prepare the states with almost perfect accuracy. The optimal protocols are
robust against dynamical noise.Comment: 4 pages, 4 figures, extended results on robustness, to appear in PR
H2 Optimal Coordination of Homogeneous Agents Subject to Limited Information Exchange
Controllers with a diagonal-plus-low-rank structure constitute a scalable
class of controllers for multi-agent systems. Previous research has shown that
diagonal-plus-low-rank control laws appear as the optimal solution to a class
of multi-agent H2 coordination problems, which arise in the control of wind
farms. In this paper we show that this result extends to the case where the
information exchange between agents is subject to limitations. We also show
that the computational effort required to obtain the optimal controller is
independent of the number of agents and provide analytical expressions that
quantify the usefulness of information exchange
A revised model of fluid transport optimization in Physarum polycephalum
Optimization of fluid transport in the slime mold Physarum polycephalum has
been the subject of several modeling efforts in recent literature. Existing
models assume that the tube adaptation mechanism in P. polycephalum's tubular
network is controlled by the sheer amount of fluid flow through the tubes. We
put forward the hypothesis that the controlling variable may instead be the
flow's pressure gradient along the tube. We carry out the stability analysis of
such a revised mathematical model for a parallel-edge network, proving that the
revised model supports the global flow-optimizing behavior of the slime mold
for a substantially wider class of response functions compared to previous
models. Simulations also suggest that the same conclusion may be valid for
arbitrary network topologies.Comment: To appear in Journal of Mathematical Biolog
Optimal suppression of defect generation during a passage across a quantum critical point
The dynamics of quantum phase transitions are inevitably accompanied by the
formation of defects when crossing a quantum critical point. For a generic
class of quantum critical systems, we solve the problem of minimizing the
production of defects through the use of a gradient-based deterministic optimal
control algorithm. By considering a finite size quantum Ising model with a
tunable global transverse field, we show that an optimal power law quench of
the transverse field across the Ising critical point works well at minimizing
the number of defects, in spite of being drawn from a subset of quench
profiles. These power law quenches are shown to be inherently robust against
noise. The optimized defect density exhibits a transition at a critical ratio
of the quench duration to the system size, which we argue coincides with the
intrinsic speed limit for quantum evolution.Comment: 5 pages, 4 figures. The first two authors contributed equally to this
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