94,098 research outputs found
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
The present article presents a summarizing view at differential-algebraic
equations (DAEs) and analyzes how new application fields and corresponding
mathematical models lead to innovations both in theory and in numerical
analysis for this problem class. Recent numerical methods for nonsmooth
dynamical systems subject to unilateral contact and friction illustrate the
topicality of this development.Comment: Preprint of Book Chapte
Discrete mechanics and optimal control for constrained systems
The equations of motion of a controlled mechanical system subject to holonomic constraints may be formulated in terms
of the states and controls by applying a constrained version of the Lagrange-d’Alembert principle. This paper derives a
structure-preserving scheme for the optimal control of such systems using, as one of the key ingredients, a discrete analogue
of that principle. This property is inherited when the system is reduced to its minimal dimension by the discrete null
space method. Together with initial and final conditions on the configuration and conjugate momentum, the reduced discrete
equations serve as nonlinear equality constraints for the minimization of a given objective functional. The algorithm yields
a sequence of discrete configurations together with a sequence of actuating forces, optimally guiding the system from the
initial to the desired final state. In particular, for the optimal control of multibody systems, a force formulation consistent
with the joint constraints is introduced. This enables one to prove the consistency of the evolution of momentum maps.
Using a two-link pendulum, the method is compared with existing methods. Further, it is applied to a satellite reorientation
maneuver and a biomotion problem
Discrete second order constrained Lagrangian systems: first results
We briefly review the notion of second order constrained (continuous) system
(SOCS) and then propose a discrete time counterpart of it, which we naturally
call discrete second order constrained system (DSOCS). To illustrate and test
numerically our model, we construct certain integrators that simulate the
evolution of two mechanical systems: a particle moving in the plane with
prescribed signed curvature, and the inertia wheel pendulum with a Lyapunov
constraint. In addition, we prove a local existence and uniqueness result for
trajectories of DSOCSs. As a first comparison of the underlying geometric
structures, we study the symplectic behavior of both SOCSs and DSOCSs.Comment: 17 pages, 6 figure
R-adaptive multisymplectic and variational integrators
Moving mesh methods (also called r-adaptive methods) are space-adaptive
strategies used for the numerical simulation of time-dependent partial
differential equations. These methods keep the total number of mesh points
fixed during the simulation, but redistribute them over time to follow the
areas where a higher mesh point density is required. There are a very limited
number of moving mesh methods designed for solving field-theoretic partial
differential equations, and the numerical analysis of the resulting schemes is
challenging. In this paper we present two ways to construct r-adaptive
variational and multisymplectic integrators for (1+1)-dimensional Lagrangian
field theories. The first method uses a variational discretization of the
physical equations and the mesh equations are then coupled in a way typical of
the existing r-adaptive schemes. The second method treats the mesh points as
pseudo-particles and incorporates their dynamics directly into the variational
principle. A user-specified adaptation strategy is then enforced through
Lagrange multipliers as a constraint on the dynamics of both the physical field
and the mesh points. We discuss the advantages and limitations of our methods.
Numerical results for the Sine-Gordon equation are also presented.Comment: 65 pages, 13 figure
Optimal Control of Underactuated Mechanical Systems: A Geometric Approach
In this paper, we consider a geometric formalism for optimal control of
underactuated mechanical systems. Our techniques are an adaptation of the
classical Skinner and Rusk approach for the case of Lagrangian dynamics with
higher-order constraints. We study a regular case where it is possible to
establish a symplectic framework and, as a consequence, to obtain a unique
vector field determining the dynamics of the optimal control problem. These
developments will allow us to develop a new class of geometric integrators
based on discrete variational calculus.Comment: 20 pages, 2 figure
Non-intrusive and structure preserving multiscale integration of stiff ODEs, SDEs and Hamiltonian systems with hidden slow dynamics via flow averaging
We introduce a new class of integrators for stiff ODEs as well as SDEs. These
integrators are (i) {\it Multiscale}: they are based on flow averaging and so
do not fully resolve the fast variables and have a computational cost
determined by slow variables (ii) {\it Versatile}: the method is based on
averaging the flows of the given dynamical system (which may have hidden slow
and fast processes) instead of averaging the instantaneous drift of assumed
separated slow and fast processes. This bypasses the need for identifying
explicitly (or numerically) the slow or fast variables (iii) {\it
Nonintrusive}: A pre-existing numerical scheme resolving the microscopic time
scale can be used as a black box and easily turned into one of the integrators
in this paper by turning the large coefficients on over a microscopic timescale
and off during a mesoscopic timescale (iv) {\it Convergent over two scales}:
strongly over slow processes and in the sense of measures over fast ones. We
introduce the related notion of two-scale flow convergence and analyze the
convergence of these integrators under the induced topology (v) {\it Structure
preserving}: for stiff Hamiltonian systems (possibly on manifolds), they can be
made to be symplectic, time-reversible, and symmetry preserving (symmetries are
group actions that leave the system invariant) in all variables. They are
explicit and applicable to arbitrary stiff potentials (that need not be
quadratic). Their application to the Fermi-Pasta-Ulam problems shows accuracy
and stability over four orders of magnitude of time scales. For stiff Langevin
equations, they are symmetry preserving, time-reversible and Boltzmann-Gibbs
reversible, quasi-symplectic on all variables and conformally symplectic with
isotropic friction.Comment: 69 pages, 21 figure
Discrete mechanics and optimal control: An analysis
The optimal control of a mechanical system is of crucial importance in many application areas. Typical examples are the determination of a time-minimal path in vehicle dynamics, a minimal energy trajectory in space mission design, or optimal motion sequences in robotics and biomechanics. In most cases, some sort of discretization of the original, infinite-dimensional optimization problem has to be performed in order to make the problem amenable to computations. The approach proposed in this paper is to directly discretize the variational description of the system's motion. The resulting optimization algorithm lets the discrete solution directly inherit characteristic structural properties from the continuous one like symmetries and integrals of the motion. We show that the DMOC (Discrete Mechanics and Optimal Control) approach is equivalent to a finite difference discretization of Hamilton's equations by a symplectic partitioned Runge-Kutta scheme and employ this fact in order to give a proof of convergence. The numerical performance of DMOC and its relationship to other existing optimal control methods are investigated
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