303,822 research outputs found
A Convergent Approximation of the Pareto Optimal Set for Finite Horizon Multiobjective Optimal Control Problems (MOC) Using Viability Theory
The objective of this paper is to provide a convergent numerical
approximation of the Pareto optimal set for finite-horizon multiobjective
optimal control problems for which the objective space is not necessarily
convex. Our approach is based on Viability Theory. We first introduce the
set-valued return function V and show that the epigraph of V is equal to the
viability kernel of a properly chosen closed set for a properly chosen
dynamics. We then introduce an approximate set-valued return function with
finite set-values as the solution of a multiobjective dynamic programming
equation. The epigraph of this approximate set-valued return function is shown
to be equal to the finite discrete viability kernel resulting from the
convergent numerical approximation of the viability kernel proposed in [4, 5].
As a result, the epigraph of the approximate set-valued return function
converges towards the epigraph of V. The approximate set-valued return function
finally provides the proposed numerical approximation of the Pareto optimal set
for every initial time and state. Several numerical examples are provided
Computational alternatives to obtain time optimal jet engine control
Two computational methods to determine an open loop time optimal control sequence for a simple single spool turbojet engine are described by a set of nonlinear differential equations. Both methods are modifications of widely accepted algorithms which can solve fixed time unconstrained optimal control problems with a free right end. Constrained problems to be considered have fixed right ends and free time. Dynamic programming is defined on a standard problem and it yields a successive approximation solution to the time optimal problem of interest. A feedback control law is obtained and it is then used to determine the corresponding open loop control sequence. The Fletcher-Reeves conjugate gradient method has been selected for adaptation to solve a nonlinear optimal control problem with state variable and control constraints
A reduced complexity numerical method for optimal gate synthesis
Although quantum computers have the potential to efficiently solve certain
problems considered difficult by known classical approaches, the design of a
quantum circuit remains computationally difficult. It is known that the optimal
gate design problem is equivalent to the solution of an associated optimal
control problem, the solution to which is also computationally intensive.
Hence, in this article, we introduce the application of a class of numerical
methods (termed the max-plus curse of dimensionality free techniques) that
determine the optimal control thereby synthesizing the desired unitary gate.
The application of this technique to quantum systems has a growth in complexity
that depends on the cardinality of the control set approximation rather than
the much larger growth with respect to spatial dimensions in approaches based
on gridding of the space, used in previous literature. This technique is
demonstrated by obtaining an approximate solution for the gate synthesis on
- a problem that is computationally intractable by grid based
approaches.Comment: 8 pages, 4 figure
Approximation algorithms for planning and control
A control system operating in a complex environment will encounter a variety of different situations, with varying amounts of time available to respond to critical events. Ideally, such a control system will do the best possible with the time available. In other words, its responses should approximate those that would result from having unlimited time for computation, where the degree of the approximation depends on the amount of time it actually has. There exist approximation algorithms for a wide variety of problems. Unfortunately, the solution to any reasonably complex control problem will require solving several computationally intensive problems. Algorithms for successive approximation are a subclass of the class of anytime algorithms, algorithms that return answers for any amount of computation time, where the answers improve as more time is allotted. An architecture is described for allocating computation time to a set of anytime algorithms, based on expectations regarding the value of the answers they return. The architecture described is quite general, producing optimal schedules for a set of algorithms under widely varying conditions
Optimal design and optimal control of structures undergoing finite rotations and elastic deformations
In this work we deal with the optimal design and optimal control of
structures undergoing large rotations. In other words, we show how to find the
corresponding initial configuration and the corresponding set of multiple load
parameters in order to recover a desired deformed configuration or some
desirable features of the deformed configuration as specified more precisely by
the objective or cost function. The model problem chosen to illustrate the
proposed optimal design and optimal control methodologies is the one of
geometrically exact beam. First, we present a non-standard formulation of the
optimal design and optimal control problems, relying on the method of Lagrange
multipliers in order to make the mechanics state variables independent from
either design or control variables and thus provide the most general basis for
developing the best possible solution procedure. Two different solution
procedures are then explored, one based on the diffuse approximation of
response function and gradient method and the other one based on genetic
algorithm. A number of numerical examples are given in order to illustrate both
the advantages and potential drawbacks of each of the presented procedures.Comment: 35 pages, 11 figure
Galerkin approximations for the optimal control of nonlinear delay differential equations
Optimal control problems of nonlinear delay differential equations (DDEs) are
considered for which we propose a general Galerkin approximation scheme built
from Koornwinder polynomials. Error estimates for the resulting
Galerkin-Koornwinder approximations to the optimal control and the value
function, are derived for a broad class of cost functionals and nonlinear DDEs.
The approach is illustrated on a delayed logistic equation set not far away
from its Hopf bifurcation point in the parameter space. In this case, we show
that low-dimensional controls for a standard quadratic cost functional can be
efficiently computed from Galerkin-Koornwinder approximations to reduce at a
nearly optimal cost the oscillation amplitude displayed by the DDE's solution.
Optimal controls computed from the Pontryagin's maximum principle (PMP) and the
Hamilton-Jacobi-Bellman equation (HJB) associated with the corresponding ODE
systems, are shown to provide numerical solutions in good agreement. It is
finally argued that the value function computed from the corresponding reduced
HJB equation provides a good approximation of that obtained from the full HJB
equation.Comment: 29 pages. This is a sequel of the arXiv preprint arXiv:1704.0042
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