A dynamic programming algorithm for input estimation on linear time-variant systems

Nordström, Lars

doi:10.1016/j.cma.2006.01.002

Cited by 37 publications

(17 citation statements)

References 9 publications

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“…Hillary and Ewins [4], Starkey and Merrill [5] and Doyle [6], involve transformation of signals and differential equations into the frequency domain, and are thus most suited for linear time-invariant systems. Time domain methods have potential to handle time-variant linear as well as non-linear systems, although a majority of presented algorithms are concerned with linear systems, as the inverse structural filter [7], the partial modal matrix method [8], and Dynamic Programming [9], to mention a few. Time domain methods suitable for treatment of non-linear systems are presented in Huang [10] and Nordberg [11].…”

Section: Introductionmentioning

confidence: 99%

A strategy for input estimation with sensitivity analysis

Nordström

Johansson

Larsson

2006

Numerical Meth Engineering

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SUMMARYThe estimation of unknown excitation or input signals from measured response is essentially the solution of an optimization problem; a function representing the input is sought such that the discrepancy between measured and simulated response is minimized. From the view of optimization it is convenient to consider the general situation where the governing differential equations may be time-variant and non-linear. Moreover, the choice of parameterization for the sought input, sampling instances of measurement data and discretization of the state problem are all decoupled, in contrast to standard methods such as Dynamic Programming.In this paper, the optimization problem is solved by seeking a stationary point of the pertinent Lagrangian functional, resulting in a Newton-type iterative algorithm. Moreover, the sensitivity of the solution with respect to measurement noise may also be estimated from a perturbation analysis of the optimality conditions. Two numerical examples show that the approach produces results in agreement with those of standard methods, however, with the additional advantage the numerical properties of the considered problem may be investigated. In particular, such knowledge may be used for choosing a proper amount of regularization.

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Section: Introductionmentioning

confidence: 99%

A strategy for input estimation with sensitivity analysis

Nordström

Johansson

Larsson

2006

Numerical Meth Engineering

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“…It was first formulated by Trujillo (1978) and it has been implemented for moving force identification problems which include zeroth order regularisation with the optimal state estimation approach (Law and Fang 2001), and in generalised solutions to moving force identification which use higher order Tikhonov regularisation (Nordström 2006, González et al 2008b. Lourens et al (2012) propose an augmented Kalman filter (AKF)…”

Section: Identification Of Dynamic Axle Forcesmentioning

confidence: 99%

“…To provide smoother solutions and a bound to the identified forces, the Tikhonov regularisation method (Tikhonov and Arsenin 1977) is included in the solution (Zhu and Law 1999, 2001a, 2001b, 2002, Nordström 2006, Law et al 2007, Gonzalez et al 2008b, Deng and Cai 2010b. Recently, optimisation techniques have been developed for moving force identification which has led to methods which are based on genetic algorithms ) (whereby interaction forces are calculated after estimating vehicle parameters) and simulated annealing genetic algorithms .…”

Section: Introductionmentioning

confidence: 99%

“…First order Tikhonov regularisation is used to decrease errors due to illconditioning and the recursive least-squares problem is solved using the Dynamic Programming technique (Trujillo 1978) which has been utilised previously in force identification problems (Law and Fang 2001, Nordström 2006, González et al 2008b. A coupled vehicle-bridge interaction (VBI) model is created in MATLAB (2005) to simulate 'measured' accelerations.…”

Section: Introductionmentioning

confidence: 99%

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A drive-by inspection system via vehicle moving force identification

O’Brien

McGetrick²,

González

2014

Smart Structures and Systems

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Publication informationSmart Structures and Systems, 13 (5) Abstract. This paper presents a novel method to carry out monitoring of transport infrastructure such as pavements and bridges through the analysis of vehicle accelerations. An algorithm is developed for the identification of dynamic vehicle-bridge interaction forces using the vehicle response. Moving force identification theory is applied to a vehicle model in order to identify these dynamic forces between the vehicle and the road and/or bridge. A coupled half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the forces. The potential of the method to identify the global bending stiffness of the bridge and to predict the pavement roughness is presented. The method is tested for a range of bridge spans using theoretical simulations and the influences of road roughness and signal noise on the accuracy of the results are investigated.

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“…These methods can mainly be classified into two categories: frequency-domain method [1,2] and time-domain method [3,4], etc. Just as their name imply, the relationship between the measurement response and the dynamic force to be estimated are formulated in frequency domain and time domain respectively in the two kinds of method.…”

Section: Introductionmentioning

confidence: 99%