15,954 research outputs found
The max-plus finite element method for solving deterministic optimal control problems: basic properties and convergence analysis
We introduce a max-plus analogue of the Petrov-Galerkin finite element method
to solve finite horizon deterministic optimal control problems. The method
relies on a max-plus variational formulation. We show that the error in the sup
norm can be bounded from the difference between the value function and its
projections on max-plus and min-plus semimodules, when the max-plus analogue of
the stiffness matrix is exactly known. In general, the stiffness matrix must be
approximated: this requires approximating the operation of the Lax-Oleinik
semigroup on finite elements. We consider two approximations relying on the
Hamiltonian. We derive a convergence result, in arbitrary dimension, showing
that for a class of problems, the error estimate is of order or , depending on the
choice of the approximation, where and are respectively the
time and space discretization steps. We compare our method with another
max-plus based discretization method previously introduced by Fleming and
McEneaney. We give numerical examples in dimension 1 and 2.Comment: 31 pages, 11 figure
Bundle-based pruning in the max-plus curse of dimensionality free method
Recently a new class of techniques termed the max-plus curse of
dimensionality-free methods have been developed to solve nonlinear optimal
control problems. In these methods the discretization in state space is avoided
by using a max-plus basis expansion of the value function. This requires
storing only the coefficients of the basis functions used for representation.
However, the number of basis functions grows exponentially with respect to the
number of time steps of propagation to the time horizon of the control problem.
This so called "curse of complexity" can be managed by applying a pruning
procedure which selects the subset of basis functions that contribute most to
the approximation of the value function. The pruning procedures described thus
far in the literature rely on the solution of a sequence of high dimensional
optimization problems which can become computationally expensive.
In this paper we show that if the max-plus basis functions are linear and the
region of interest in state space is convex, the pruning problem can be
efficiently solved by the bundle method. This approach combining the bundle
method and semidefinite formulations is applied to the quantum gate synthesis
problem, in which the state space is the special unitary group (which is
non-convex). This is based on the observation that the convexification of the
unitary group leads to an exact relaxation. The results are studied and
validated via examples
Multigrid methods for two-player zero-sum stochastic games
We present a fast numerical algorithm for large scale zero-sum stochastic
games with perfect information, which combines policy iteration and algebraic
multigrid methods. This algorithm can be applied either to a true finite state
space zero-sum two player game or to the discretization of an Isaacs equation.
We present numerical tests on discretizations of Isaacs equations or
variational inequalities. We also present a full multi-level policy iteration,
similar to FMG, which allows to improve substantially the computation time for
solving some variational inequalities.Comment: 31 page
Curse of dimensionality reduction in max-plus based approximation methods: theoretical estimates and improved pruning algorithms
Max-plus based methods have been recently developed to approximate the value
function of possibly high dimensional optimal control problems. A critical step
of these methods consists in approximating a function by a supremum of a small
number of functions (max-plus "basis functions") taken from a prescribed
dictionary. We study several variants of this approximation problem, which we
show to be continuous versions of the facility location and -center
combinatorial optimization problems, in which the connection costs arise from a
Bregman distance. We give theoretical error estimates, quantifying the number
of basis functions needed to reach a prescribed accuracy. We derive from our
approach a refinement of the curse of dimensionality free method introduced
previously by McEneaney, with a higher accuracy for a comparable computational
cost.Comment: 8pages 5 figure
The max-plus finite element method for optimal control problems: further approximation results
We develop the max-plus finite element method to solve finite horizon
deterministic optimal control problems. This method, that we introduced in a
previous work, relies on a max-plus variational formulation, and exploits the
properties of projectors on max-plus semimodules. We prove here a convergence
result, in arbitrary dimension, showing that for a subclass of problems, the
error estimate is of order , where and
are the time and space steps respectively. We also show how the
max-plus analogues of the mass and stiffness matrices can be computed by convex
optimization, even when the global problem is non convex. We illustrate the
method by numerical examples in dimension 2.Comment: 13 pages, 2 figure
A max-plus finite element method for solving finite horizon deterministic optimal control problems
We introduce a max-plus analogue of the Petrov-Galerkin finite element
method, to solve finite horizon deterministic optimal control problems. The
method relies on a max-plus variational formulation, and exploits the
properties of projectors on max-plus semimodules. We obtain a nonlinear
discretized semigroup, corresponding to a zero-sum two players game. We give an
error estimate of order , for a
subclass of problems in dimension 1. We compare our method with a max-plus
based discretization method previously introduced by Fleming and McEneaney.Comment: 13 pages, 5 figure
Robust Model Predictive Control via Scenario Optimization
This paper discusses a novel probabilistic approach for the design of robust
model predictive control (MPC) laws for discrete-time linear systems affected
by parametric uncertainty and additive disturbances. The proposed technique is
based on the iterated solution, at each step, of a finite-horizon optimal
control problem (FHOCP) that takes into account a suitable number of randomly
extracted scenarios of uncertainty and disturbances, followed by a specific
command selection rule implemented in a receding horizon fashion. The scenario
FHOCP is always convex, also when the uncertain parameters and disturbance
belong to non-convex sets, and irrespective of how the model uncertainty
influences the system's matrices. Moreover, the computational complexity of the
proposed approach does not depend on the uncertainty/disturbance dimensions,
and scales quadratically with the control horizon. The main result in this
paper is related to the analysis of the closed loop system under
receding-horizon implementation of the scenario FHOCP, and essentially states
that the devised control law guarantees constraint satisfaction at each step
with some a-priori assigned probability p, while the system's state reaches the
target set either asymptotically, or in finite time with probability at least
p. The proposed method may be a valid alternative when other existing
techniques, either deterministic or stochastic, are not directly usable due to
excessive conservatism or to numerical intractability caused by lack of
convexity of the robust or chance-constrained optimization problem.Comment: This manuscript is a preprint of a paper accepted for publication in
the IEEE Transactions on Automatic Control, with DOI:
10.1109/TAC.2012.2203054, and is subject to IEEE copyright. The copy of
record will be available at http://ieeexplore.ieee.or
Tropical Kraus maps for optimal control of switched systems
Kraus maps (completely positive trace preserving maps) arise classically in
quantum information, as they describe the evolution of noncommutative
probability measures. We introduce tropical analogues of Kraus maps, obtained
by replacing the addition of positive semidefinite matrices by a multivalued
supremum with respect to the L\"owner order. We show that non-linear
eigenvectors of tropical Kraus maps determine piecewise quadratic
approximations of the value functions of switched optimal control problems.
This leads to a new approximation method, which we illustrate by two
applications: 1) approximating the joint spectral radius, 2) computing
approximate solutions of Hamilton-Jacobi PDE arising from a class of switched
linear quadratic problems studied previously by McEneaney. We report numerical
experiments, indicating a major improvement in terms of scalability by
comparison with earlier numerical schemes, owing to the "LMI-free" nature of
our method.Comment: 15 page
- …