8,915 research outputs found
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising
in prediction error parameter estimation of linear and nonlinear systems. We
show that for regions of the parameter space where the model is not
contractive, the Lipschitz constant and -smoothness of the objective
function might blow up exponentially with the simulation length, making it hard
to numerically find minima within those regions or, even, to escape from them.
In addition to providing theoretical understanding of this problem, this paper
also proposes the use of multiple shooting as a viable solution. The proposed
method minimizes the error between a prediction model and the observed values.
Rather than running the prediction model over the entire dataset, multiple
shooting splits the data into smaller subsets and runs the prediction model
over each subset, making the simulation length a design parameter and making it
possible to solve problems that would be infeasible using a standard approach.
The equivalence to the original problem is obtained by including constraints in
the optimization. The new method is illustrated by estimating the parameters of
nonlinear systems with chaotic or unstable behavior, as well as neural
networks. We also present a comparative analysis of the proposed method with
multi-step-ahead prediction error minimization
Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control
This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and potentially nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a generic, spatial branch-and-bound algorithm. A new operation, called lifting, is introduced, which refines the control parameterization via a Gram-Schmidt orthogonalization process, while simultaneously eliminating control subregions that are either infeasible or that provably cannot contain any global optima. Conditions are given under which the image of the control parameterization error in the state space contracts exponentially as the parameterization order is increased, thereby making the lifting operation efficient. A computational technique based on ellipsoidal calculus is also developed that satisfies these conditions. The practical applicability of branch-and-lift is illustrated in a numerical example. © 2013 Springer Science+Business Media New York
Prediction of stable walking for a toy that cannot stand
Previous experiments [M. J. Coleman and A. Ruina, Phys. Rev. Lett. 80, 3658
(1998)] showed that a gravity-powered toy with no control and which has no
statically stable near-standing configurations can walk stably. We show here
that a simple rigid-body statically-unstable mathematical model based loosely
on the physical toy can predict stable limit-cycle walking motions. These
calculations add to the repertoire of rigid-body mechanism behaviors as well as
further implicating passive-dynamics as a possible contributor to stability of
animal motions.Comment: Note: only corrections so far have been fixing typo's in these
comments. 3 pages, 2 eps figures, uses epsf.tex, revtex.sty, amsfonts.sty,
aps.sty, aps10.sty, prabib.sty; Accepted for publication in Phys. Rev. E.
4/9/2001 ; information about Andy Ruina's lab (including Coleman's, Garcia's
and Ruina's other publications and associated video clips) can be found at:
http://www.tam.cornell.edu/~ruina/hplab/index.html and more about Georg
Bock's Simulation Group with whom Katja Mombaur is affiliated can be found at
http://www.iwr.uni-heidelberg.de/~agboc
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