829 research outputs found
Stability and Performance Verification of Optimization-based Controllers
This paper presents a method to verify closed-loop properties of
optimization-based controllers for deterministic and stochastic constrained
polynomial discrete-time dynamical systems. The closed-loop properties amenable
to the proposed technique include global and local stability, performance with
respect to a given cost function (both in a deterministic and stochastic
setting) and the gain. The method applies to a wide range of
practical control problems: For instance, a dynamical controller (e.g., a PID)
plus input saturation, model predictive control with state estimation, inexact
model and soft constraints, or a general optimization-based controller where
the underlying problem is solved with a fixed number of iterations of a
first-order method are all amenable to the proposed approach.
The approach is based on the observation that the control input generated by
an optimization-based controller satisfies the associated Karush-Kuhn-Tucker
(KKT) conditions which, provided all data is polynomial, are a system of
polynomial equalities and inequalities. The closed-loop properties can then be
analyzed using sum-of-squares (SOS) programming
Standard Errors for Calibrated Parameters
Calibration, the practice of choosing the parameters of a structural model to
match certain empirical moments, can be viewed as minimum distance estimation.
Existing standard error formulas for such estimators require a consistent
estimate of the correlation structure of the empirical moments, which is often
unavailable in practice. Instead, the variances of the individual empirical
moments are usually readily estimable. Using only these variances, we derive
conservative standard errors and confidence intervals for the structural
parameters that are valid even under the worst-case correlation structure. In
the over-identified case, we show that the moment weighting scheme that
minimizes the worst-case estimator variance amounts to a moment selection
problem with a simple solution. Finally, we develop tests of over-identifying
or parameter restrictions. We apply our methods empirically to a model of menu
cost pricing for multi-product firms and to a heterogeneous agent New Keynesian
model
Convex Identifcation of Stable Dynamical Systems
This thesis concerns the scalable application of convex optimization to data-driven modeling of dynamical systems, termed system identi cation in the control community. Two problems commonly arising in system identi cation are model instability (e.g. unreliability of long-term, open-loop predictions), and nonconvexity of quality-of- t criteria, such as simulation error (a.k.a. output error). To address these problems, this thesis presents convex parametrizations of stable dynamical systems, convex quality-of- t criteria, and e cient algorithms to optimize the latter over the former. In particular, this thesis makes extensive use of Lagrangian relaxation, a technique for generating convex approximations to nonconvex optimization problems. Recently, Lagrangian relaxation has been used to approximate simulation error and guarantee nonlinear model stability via semide nite programming (SDP), however, the resulting SDPs have large dimension, limiting their practical utility. The rst contribution of this thesis is a custom interior point algorithm that exploits structure in the problem to signi cantly reduce computational complexity. The new algorithm enables empirical comparisons to established methods including Nonlinear ARX, in which superior generalization to new data is demonstrated. Equipped with this algorithmic machinery, the second contribution of this thesis is the incorporation of model stability constraints into the maximum likelihood framework. Speci - cally, Lagrangian relaxation is combined with the expectation maximization (EM) algorithm to derive tight bounds on the likelihood function, that can be optimized over a convex parametrization of all stable linear dynamical systems. Two di erent formulations are presented, one of which gives higher delity bounds when disturbances (a.k.a. process noise) dominate measurement noise, and vice versa. Finally, identi cation of positive systems is considered. Such systems enjoy substantially simpler stability and performance analysis compared to the general linear time-invariant iv Abstract (LTI) case, and appear frequently in applications where physical constraints imply nonnegativity of the quantities of interest. Lagrangian relaxation is used to derive new convex parametrizations of stable positive systems and quality-of- t criteria, and substantial improvements in accuracy of the identi ed models, compared to existing approaches based on weighted equation error, are demonstrated. Furthermore, the convex parametrizations of stable systems based on linear Lyapunov functions are shown to be amenable to distributed optimization, which is useful for identi cation of large-scale networked dynamical systems
Contributions to fuzzy polynomial techniques for stability analysis and control
The present thesis employs fuzzy-polynomial control techniques in order to
improve the stability analysis and control of nonlinear systems. Initially, it
reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems,
such as the more relevant results about polynomial and fuzzy polynomial
systems. The basic framework uses fuzzy polynomial models by Taylor series
and sum-of-squares techniques (semidefinite programming) in order to obtain
stability guarantees.
The contributions of the thesis are:
Âż Improved domain of attraction estimation of nonlinear systems for both
continuous-time and discrete-time cases. An iterative methodology based
on invariant-set results is presented for obtaining polynomial boundaries
of such domain of attraction.
Âż Extension of the above problem to the case with bounded persistent disturbances
acting. Different characterizations of inescapable sets with
polynomial boundaries are determined.
Âż State estimation: extension of the previous results in literature to the
case of fuzzy observers with polynomial gains, guaranteeing stability of
the estimation error and inescapability in a subset of the zone where the
model is valid.
Âż Proposal of a polynomial Lyapunov function with discrete delay in order
to improve some polynomial control designs from literature. Preliminary
extension to the fuzzy polynomial case.
Last chapters present a preliminary experimental work in order to check
and validate the theoretical results on real platforms in the future.Pitarch Pérez, JL. (2013). Contributions to fuzzy polynomial techniques for stability analysis and control [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34773TESI
Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
This paper proposes an algorithmic framework for solving parametric
optimization problems which we call adjoint-based predictor-corrector
sequential convex programming. After presenting the algorithm, we prove a
contraction estimate that guarantees the tracking performance of the algorithm.
Two variants of this algorithm are investigated. The first one can be used to
solve nonlinear programming problems while the second variant is aimed to treat
online parametric nonlinear programming problems. The local convergence of
these variants is proved. An application to a large-scale benchmark problem
that originates from nonlinear model predictive control of a hydro power plant
is implemented to examine the performance of the algorithms.Comment: This manuscript consists of 25 pages and 7 figure
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