2,265,943 research outputs found
Optimal Flood Control
A mathematical model for optimal control of the water levels in a chain of
reservoirs is studied. Some remarks regarding sensitivity with respect to the time horizon, terminal cost and forecast of inflow are made
Deception in Optimal Control
In this paper, we consider an adversarial scenario where one agent seeks to
achieve an objective and its adversary seeks to learn the agent's intentions
and prevent the agent from achieving its objective. The agent has an incentive
to try to deceive the adversary about its intentions, while at the same time
working to achieve its objective. The primary contribution of this paper is to
introduce a mathematically rigorous framework for the notion of deception
within the context of optimal control. The central notion introduced in the
paper is that of a belief-induced reward: a reward dependent not only on the
agent's state and action, but also adversary's beliefs. Design of an optimal
deceptive strategy then becomes a question of optimal control design on the
product of the agent's state space and the adversary's belief space. The
proposed framework allows for deception to be defined in an arbitrary control
system endowed with a reward function, as well as with additional
specifications limiting the agent's control policy. In addition to defining
deception, we discuss design of optimally deceptive strategies under
uncertainties in agent's knowledge about the adversary's learning process. In
the latter part of the paper, we focus on a setting where the agent's behavior
is governed by a Markov decision process, and show that the design of optimally
deceptive strategies under lack of knowledge about the adversary naturally
reduces to previously discussed problems in control design on partially
observable or uncertain Markov decision processes. Finally, we present two
examples of deceptive strategies: a "cops and robbers" scenario and an example
where an agent may use camouflage while moving. We show that optimally
deceptive strategies in such examples follow the intuitive idea of how to
deceive an adversary in the above settings
Discrete Variational Optimal Control
This paper develops numerical methods for optimal control of mechanical
systems in the Lagrangian setting. It extends the theory of discrete mechanics
to enable the solutions of optimal control problems through the discretization
of variational principles. The key point is to solve the optimal control
problem as a variational integrator of a specially constructed
higher-dimensional system. The developed framework applies to systems on
tangent bundles, Lie groups, underactuated and nonholonomic systems with
symmetries, and can approximate either smooth or discontinuous control inputs.
The resulting methods inherit the preservation properties of variational
integrators and result in numerically robust and easily implementable
algorithms. Several theoretical and a practical examples, e.g. the control of
an underwater vehicle, will illustrate the application of the proposed
approach.Comment: 30 pages, 6 figure
Mean-Field Optimal Control
We introduce the concept of {\it mean-field optimal control} which is the
rigorous limit process connecting finite dimensional optimal control problems
with ODE constraints modeling multi-agent interactions to an infinite
dimensional optimal control problem with a constraint given by a PDE of
Vlasov-type, governing the dynamics of the probability distribution of
interacting agents. While in the classical mean-field theory one studies the
behavior of a large number of small individuals {\it freely interacting} with
each other, by simplifying the effect of all the other individuals on any given
individual by a single averaged effect, we address the situation where the
individuals are actually influenced also by an external {\it policy maker}, and
we propagate its effect for the number of individuals going to infinity. On
the one hand, from a modeling point of view, we take into account also that the
policy maker is constrained to act according to optimal strategies promoting
its most parsimonious interaction with the group of individuals. This will be
realized by considering cost functionals including -norm terms penalizing
a broadly distributed control of the group, while promoting its sparsity. On
the other hand, from the analysis point of view, and for the sake of
generality, we consider broader classes of convex control penalizations. In
order to develop this new concept of limit rigorously, we need to carefully
combine the classical concept of mean-field limit, connecting the finite
dimensional system of ODE describing the dynamics of each individual of the
group to the PDE describing the dynamics of the respective probability
distribution, with the well-known concept of -convergence to show that
optimal strategies for the finite dimensional problems converge to optimal
strategies of the infinite dimensional problem.Comment: 31 page
Localized LQR Optimal Control
This paper introduces a receding horizon like control scheme for localizable
distributed systems, in which the effect of each local disturbance is limited
spatially and temporally. We characterize such systems by a set of linear
equality constraints, and show that the resulting feasibility test can be
solved in a localized and distributed way. We also show that the solution of
the local feasibility tests can be used to synthesize a receding horizon like
controller that achieves the desired closed loop response in a localized manner
as well. Finally, we formulate the Localized LQR (LLQR) optimal control problem
and derive an analytic solution for the optimal controller. Through a numerical
example, we show that the LLQR optimal controller, with its constraints on
locality, settling time, and communication delay, can achieve similar
performance as an unconstrained H2 optimal controller, but can be designed and
implemented in a localized and distributed way.Comment: Extended version for 2014 CDC submissio
Infinite horizon sparse optimal control
A class of infinite horizon optimal control problems involving -type
cost functionals with is discussed. The existence of optimal
controls is studied for both the convex case with and the nonconvex case
with , and the sparsity structure of the optimal controls promoted by
the -type penalties is analyzed. A dynamic programming approach is
proposed to numerically approximate the corresponding sparse optimal
controllers
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