Recursive forgetting algorithms

Parkum, Jens; Poulsen, Niels Kjølstad; Holst, Jan

doi:10.1080/00207179208934228

Cited by 98 publications

(44 citation statements)

References 4 publications

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“…In fact, P\ R can also be generated directly as the solution of an ODE analogous to (18). This saves computing the inverse continually to obtain the latest estimate of ) R .…”

Section: Remark 24mentioning

confidence: 99%

Generalized forgetting functions for on‐line least‐squares identification of time‐varying systems

Mahony

Lozano

2001

Adaptive Control & Signal

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The problem of on-line identi"cation of a parametric model for continuous-time, time-varying systems is considered via the minimization of a least-squares criterion with a forgetting function. The proposed forgetting function depends on two time-varying parameters which play crucial roles in the stability analysis of the method. The analysis leads to the consideration of a Lyapunov function for the identi"cation algorithm that incorporates both prediction error and parameter convergence measures. A theorem is proved showing "nite time convergence of the Lyapunov function to a neighbourhood of zero, the size of which depends on the evolution of the time-varying error terms in the parametric model representation.

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“…In fact, P\ R can also be generated directly as the solution of an ODE analogous to (18). This saves computing the inverse continually to obtain the latest estimate of ) R .…”

Section: Remark 24mentioning

confidence: 99%

Generalized forgetting functions for on‐line least‐squares identification of time‐varying systems

Mahony

Lozano

2001

Adaptive Control & Signal

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“…However if the sequence of regression vectors (k) is not su$ciently exciting, the eigenvalues of P(k) will grow without limit. The resulting problems are discussed for example in Parkum, Poulsen and Holst (1992) and in Bittanti, Bolzern and Campi (1990). In order to solve this problem Parkum et al (1992) proposed the following two steps procedure to implement (11):…”

Section: Selective Forgetting Methods For Least-squares Recursive Estimentioning

confidence: 99%

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Pereira

Henriques

Dourado

2000

Control Engineering Practice

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In this work a practical study evaluates two parametric modelling approaches * linear and non-linear (neural) * for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and di!erent types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.

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“…These will not all be discussed in detail here, but can be found in the following references. [30][31][32][33][34] One such technique -similar to that of Parkum et al…”

Section: Reducing Laukf Divergence In the Steady Statementioning

confidence: 99%

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

Morrison

Cebon

2016

Vehicle System Dynamics

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Various active safety systems proposed for articulated heavy goods vehicles (HGVs) require an accurate estimate of vehicle sideslip angle. However in contrast to passenger cars, there has been minimal published research on sideslip estimation for articulated HGVs. Stateof-the-art observers, which rely on linear vehicle models, perform poorly when manoeuvring near the limits of tyre adhesion. This paper investigates three nonlinear Kalman filters (KFs) for estimating the tractor sideslip angle of a tractor-semitrailer. These are compared to the current state-of-the-art, through computer simulations and vehicle test data. An unscented KF using a 5 degrees-of-freedom single-track vehicle model with linear adaptive tyres is found to substantially outperform the state-of-the-art linear KF across a range of test manoeuvres on different surfaces, both at constant speed and during emergency braking. Robustness of the observer to parameter uncertainty is also demonstrated. ARTICLE HISTORY

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Recursive forgetting algorithms

Cited by 98 publications

References 4 publications

Generalized forgetting functions for on‐line least‐squares identification of time‐varying systems

Generalized forgetting functions for on‐line least‐squares identification of time‐varying systems

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Sideslip estimation for articulated heavy vehicles at the limits of adhesion

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