3,405 research outputs found
Mapping prior information onto LMI eigenvalue-regions for discrete-time subspace identification
In subspace identification, prior information can be used to constrain the
eigenvalues of the estimated state-space model by defining corresponding LMI
regions. In this paper, first we argue on what kind of practical information
can be extracted from historical data or step-response experiments to possibly
improve the dynamical properties of the corresponding model and, also, on how
to mitigate the effect of the uncertainty on such information. For instance,
prior knowledge regarding the overshoot, the period between damped oscillations
and settling time may be useful to constraint the possible locations of the
eigenvalues of the discrete-time model. Then, we show how to map the prior
information onto LMI regions and, when the obtaining regions are non-convex, to
obtain convex approximations.Comment: Under revie
Identification of Nonlinear Normal Modes of Engineering Structures under Broadband Forcing
The objective of the present paper is to develop a two-step methodology
integrating system identification and numerical continuation for the
experimental extraction of nonlinear normal modes (NNMs) under broadband
forcing. The first step processes acquired input and output data to derive an
experimental state-space model of the structure. The second step converts this
state-space model into a model in modal space from which NNMs are computed
using shooting and pseudo-arclength continuation. The method is demonstrated
using noisy synthetic data simulated on a cantilever beam with a
hardening-softening nonlinearity at its free end.Comment: Journal pape
Topics in Machining with Industrial Robot Manipulators and Optimal Motion Control
Two main topics are considered in this thesis: Machining with industrial robot manipulators and optimal motion control of robots and vehicles. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of the strong process forces required for the task. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, may significantly degrade the achieved machining accuracy of the manufactured part. In this thesis, a macro/micro-manipulator configuration is considered to the purpose of increasing the milling accuracy. In particular, a model-based control architecture is developed for control of the macro/micro-manipulator setup. The considered approach is validated by experimental results from extensive milling experiments in aluminium and steel. Related to the problem of high-accuracy milling is the topic of robot modeling. To this purpose, two different approaches are considered; modeling of the quasi-static joint dynamics and dynamic compliance modeling. The first problem is approached by an identification method for determining the joint stiffness and backlash. The second problem is approached by using gray-box identification based on subspace-identification methods. Both identification algorithms are evaluated experimentally. Finally, online state estimation is considered as a means to determine the workspace position and orientation of the robot tool. Kalman Filters and Rao-Blackwellized Particle Filters are employed to the purpose of sensor fusion of internal robot measurements and measurements from an inertial measurement unit for estimation of the desired states. The approaches considered are fully implemented and evaluated on experimental data. The second part of the thesis discusses optimal motion control applied to robot manipulators and road vehicles. A control architecture for online control of a robot manipulator in high-performance path tracking is developed, and the architecture is evaluated in extensive simulations. The main characteristic of the control strategy is that it combines coordinated feedback control along both the tangential and transversal directions of the path; this separation is achieved in the framework of natural coordinates. One motivation for research on optimal control of road vehicles in time-critical maneuvers is the desire to develop improved vehicle-safety systems. In this thesis, a method for solving optimal maneuvering problems using nonlinear optimization is discussed. More specifically, vehicle and tire modeling and the optimization formulations required to get useful solutions to these problems are investigated. The considered method is evaluated on different combinations of chassis and tire models, in maneuvers under different road conditions, and for investigation of optimal maneuvers in systems for electronic stability control. The obtained optimization results in simulations are evaluated and compared
Identifying Position-Dependent Mechanical Systems: A Modal Approach Applied to a Flexible Wafer Stage
Increasingly stringent performance requirements for motion control
necessitate the use of increasingly detailed models of the system behavior.
Motion systems inherently move, therefore, spatio-temporal models of the
flexible dynamics are essential. In this paper, a two-step approach for the
identification of the spatio-temporal behavior of mechanical systems is
developed and applied to a lightweight prototype industrial wafer stage. The
proposed approach exploits a modal modeling framework and combines recently
developed powerful linear time invariant (LTI) identification tools with a
spline-based mode-shape interpolation approach to estimate the spatial system
behavior. The experimental results for the wafer stage application confirm the
suitability of the proposed approach for the identification of complex
position-dependent mechanical systems, and its potential for motion control
performance improvements
Stochastic dynamical system identification applied to combustor stability margin assessment
A new approach was developed to determine the operational stability margin of a laboratory scale combustor. Applying modern and robust techniques and tools from Dynamical System Theory, the approach was based on three basic steps. In the first step, a gray-box thermoacoustical model for the combustor was derived. The second step consisted in applying System Identification techniques to experimental data in order to validate the model and estimate its parameters. The application of these techniques to experimental data under different operating conditions allowed us to determine the functional dependence of the model parameters upon changes in an experimental control parameter. Finally, the third step consisted in using that functional dependence to predict the response of the system at different operating conditions and, ultimately, estimate its operational stability margin. The results indicated that a low-order stochastic non-linear model, including two excited modes, has been identified and the combustor operational stability margin could be estimated by applying a continuation method.Ph.D.Committee Chair: Zinn, Ben; Committee Member: Ferri, Aldo; Committee Member: Lieuwen, Timothy; Committee Member: Prasad, J. V. R.; Committee Member: Ruzzene, Massim
Modeling and experimental identification of vibrating structures: localized and distributed nonlinearities
L'abstract è presente nell'allegato / the abstract is in the attachmen
Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines
Microturbines (MTs) are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes, which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of MTs. Considering the time-scale difference of various dynamic processes occurring within MTs, the electromechanical subsystem is treated as a fast quasi-linear process, while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics, and produce a good prediction of the coupled thermal and electric outputs in various operating modes
Completeness of Lyapunov Abstraction
In this work, we continue our study on discrete abstractions of dynamical
systems. To this end, we use a family of partitioning functions to generate an
abstraction. The intersection of sub-level sets of the partitioning functions
defines cells, which are regarded as discrete objects. The union of cells makes
up the state space of the dynamical systems. Our construction gives rise to a
combinatorial object - a timed automaton. We examine sound and complete
abstractions. An abstraction is said to be sound when the flow of the time
automata covers the flow lines of the dynamical systems. If the dynamics of the
dynamical system and the time automaton are equivalent, the abstraction is
complete.
The commonly accepted paradigm for partitioning functions is that they ought
to be transversal to the studied vector field. We show that there is no
complete partitioning with transversal functions, even for particular dynamical
systems whose critical sets are isolated critical points. Therefore, we allow
the directional derivative along the vector field to be non-positive in this
work. This considerably complicates the abstraction technique. For
understanding dynamical systems, it is vital to study stable and unstable
manifolds and their intersections. These objects appear naturally in this work.
Indeed, we show that for an abstraction to be complete, the set of critical
points of an abstraction function shall contain either the stable or unstable
manifold of the dynamical system.Comment: In Proceedings HAS 2013, arXiv:1308.490
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