5,169 research outputs found
A Convex Feasibility Approach to Anytime Model Predictive Control
This paper proposes to decouple performance optimization and enforcement of
asymptotic convergence in Model Predictive Control (MPC) so that convergence to
a given terminal set is achieved independently of how much performance is
optimized at each sampling step. By embedding an explicit decreasing condition
in the MPC constraints and thanks to a novel and very easy-to-implement convex
feasibility solver proposed in the paper, it is possible to run an outer
performance optimization algorithm on top of the feasibility solver and
optimize for an amount of time that depends on the available CPU resources
within the current sampling step (possibly going open-loop at a given sampling
step in the extreme case no resources are available) and still guarantee
convergence to the terminal set. While the MPC setup and the solver proposed in
the paper can deal with quite general classes of functions, we highlight the
synthesis method and show numerical results in case of linear MPC and
ellipsoidal and polyhedral terminal sets.Comment: 8 page
GPU-accelerated stochastic predictive control of drinking water networks
Despite the proven advantages of scenario-based stochastic model predictive
control for the operational control of water networks, its applicability is
limited by its considerable computational footprint. In this paper we fully
exploit the structure of these problems and solve them using a proximal
gradient algorithm parallelizing the involved operations. The proposed
methodology is applied and validated on a case study: the water network of the
city of Barcelona.Comment: 11 pages in double column, 7 figure
Optimization algorithms for the solution of the frictionless normal contact between rough surfaces
This paper revisits the fundamental equations for the solution of the
frictionless unilateral normal contact problem between a rough rigid surface
and a linear elastic half-plane using the boundary element method (BEM). After
recasting the resulting Linear Complementarity Problem (LCP) as a convex
quadratic program (QP) with nonnegative constraints, different optimization
algorithms are compared for its solution: (i) a Greedy method, based on
different solvers for the unconstrained linear system (Conjugate Gradient CG,
Gauss-Seidel, Cholesky factorization), (ii) a constrained CG algorithm, (iii)
the Alternating Direction Method of Multipliers (ADMM), and () the
Non-Negative Least Squares (NNLS) algorithm, possibly warm-started by
accelerated gradient projection steps or taking advantage of a loading history.
The latter method is two orders of magnitude faster than the Greedy CG method
and one order of magnitude faster than the constrained CG algorithm. Finally,
we propose another type of warm start based on a refined criterion for the
identification of the initial trial contact domain that can be used in
conjunction with all the previous optimization algorithms. This method, called
Cascade Multi-Resolution (CMR), takes advantage of physical considerations
regarding the scaling of the contact predictions by changing the surface
resolution. The method is very efficient and accurate when applied to real or
numerically generated rough surfaces, provided that their power spectral
density function is of power-law type, as in case of self-similar fractal
surfaces.Comment: 38 pages, 11 figure
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