Identification of nonlinear vibrating structures: Part I -- Formulation

A self-starting multistage, time-domain procedure is presented for the identification of nonlinear, multi-degree-of-freedom systems undergoing free oscillations or subjected to arbitrary direct force excitations and/or nonuniform support motions. Recursive least-squares parameter estimation methods combined with nonparametric identification techniques are used to represent, with sufficient accuracy, the identified system in a form that allows the convenient prediction of its transient response under excitations that differ from the test signals. The utility of this procedure is demonstrated in a companion paper

Real-time flutter identification

The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability

Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold

We formulate the problem of estimating the motion of a rigid object viewed under perspective projection as the identification of a dynamic model in Exterior Differential form with parameters on a topological manifold. We first describe a general method for recursive identification of nonlinear implicit systems using prediction error criteria. The parameters are allowed to move slowly on some topological (not necessarily smooth) manifold. The basic recursion is solved in two different ways: one is based on a simple extension of the traditional Kalman Filter to nonlinear and implicit measurement constraints, the other may be regarded as a generalized "Gauss-Newton" iteration, akin to traditional Recursive Prediction Error Method techniques in linear identification. A derivation of the "Implicit Extended Kalman Filter" (IEKF) is reported in the appendix. The ID framework is then applied to solving the visual motion problem: it indeed is possible to characterize it in terms of identification of an Exterior Differential System with parameters living on a C0 topological manifold, called the "essential manifold". We consider two alternative estimation paradigms. The first is in the local coordinates of the essential manifold: we estimate the state of a nonlinear implicit model on a linear space. The second is obtained by a linear update on the (linear) embedding space followed by a projection onto the essential manifold. These schemes proved successful in performing the motion estimation task, as we show in experiments on real and noisy synthetic image sequences

Online identification of a two-mass system in frequency domain using a Kalman filter

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

Adaptive Optimal Control The Thinking Man's GPC

Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments

Recursive identification of ship manoeuvring dynamics and hydrodynamics

This article presents new applications of recursive identification methods to estimation of ship manoeuvring dynamics and hydrodynamics. A vessel operated in sea water can be represented by a mathematical model with unknown coefficients called ship manoeuvring dynamic coefficients or hydrodynamic coefficients. Computer simulation and full scale experiments on board a small vessel verify the feasibility of the methods and show that the estimated parameters converge very well. At the Australian Maritime College there are many model vessels without any mathematical models. Hydrodynamic coefficients of these model vessels are determined by experiments to be conducted with hydrodynamics facilities at the College and by the recursive estimation methods we develop. References W. W. Zhou, D. B. Cherchas, S. Calisal and G. Rohling. Identification of rudder-yaw and -roll steering model. Unknown source. 93--101. 1986. doi:10.1002/oca.4660150203. W. W. Zhou and M. Blanke. Identification of a class of nonlinear state space model using the recursive prediction error techniques. IEEE Transactions on Automatic Control. 34(2), 312--316, UK. 1986. C. C. Hang, T. H. Lee and W. K. Ho. Adaptive control. Instrument Society of America. USA. 1993. L. Ljung and T. Soderstrom. Theory and practice of recursive identification. MIT Press, MA, USA. 1983. H. D. Nguyen. Recursive optimal manoeuvring systems for maritime search and rescue mission. Proceedings of MTS/IEEE TECHNO-OCEAN'04 (OTO'04). 911--918, Kobe, Japan. 2004. http://ieeexplore.ieee.org/iel5/9636/30461/01405599.pdf H. D. Nguyen. Self-tuning pole assignment and optimal control systems for ships, Ph.D thesis. Tokyo University of Marine Science and Technology, Tokyo, Japan. 2001. W. W. Zhou. Linear and nonlinear recursive prediction error methods in state space model. Proceedings of 8th IFAC Symposium on Identification and Systems Parameter Estimation. 1092--1099, Beijing, China. 1988

Perfect tag identification protocol in RFID networks

Radio Frequency IDentification (RFID) systems are becoming more and more popular in the field of ubiquitous computing, in particular for objects identification. An RFID system is composed by one or more readers and a number of tags. One of the main issues in an RFID network is the fast and reliable identification of all tags in the reader range. The reader issues some queries, and tags properly answer. Then, the reader must identify the tags from such answers. This is crucial for most applications. Since the transmission medium is shared, the typical problem to be faced is a MAC-like one, i.e. to avoid or limit the number of tags transmission collisions. We propose a protocol which, under some assumptions about transmission techniques, always achieves a 100% perfomance. It is based on a proper recursive splitting of the concurrent tags sets, until all tags have been identified. The other approaches present in literature have performances of about 42% in the average at most. The counterpart is a more sophisticated hardware to be deployed in the manufacture of low cost tags.Comment: 12 pages, 1 figur