Open AccessProceedings Article10.1109/MED.2009.5164628

Model based nonlinear iterative learning control: A constrained Gauss-Newton approach

- 24 Jun 2009

- pp 718-723

TL;DR: In this paper, a nonlinear iterative learning control (ILC) algorithm is proposed to solve the model inversion problem, which consists of finding the input signal corresponding to a given output signal.

Abstract: A new method is proposed to solve the model inversion problem that is part of model based iterative learning control (ILC) for nonlinear systems. The model inversion problem consists of finding the input signal corresponding to a given output signal. This problem is formulated as a nonlinear dynamic optimization problem in time domain and solved efficiently using a constrained Gauss-Newton algorithm. A nonlinear ILC algorithm based on this model inversion approach is validated numerically and experimentally. The considered application is an electric circuit described by a polynomial nonlinear state-space model. The nonlinear ILC algorithm shows fast convergence and accurate tracking control.

AI Agents for this Paper

Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps

Most frequently asked questions

1. What are the contributions in "Model based nonlinear iterative learning control: a constrained gauss-newton approach" ?

In this paper, a nonlinear ILC algorithm based on model inversion approach is validated numerically and experimentally.

Fig. 5. Experimental validation of nonlinear ILC: convergence of the relative tracking error (left), and reduction of the iteration gain (right)

Fig. 6. Experimental validation of nonlinear ILC: frequency spectra of the reference signal (x), the measurement noise (+), the initial tracking error (*) and the tracking error after 10 iterations (*) (top), and the reference signal, initial and final tracking error in time domain (bottom)

Fig. 4. Comparison of linear and nonlinear ILC in simulation: convergence of the relative tracking error (left), and reduction of the iteration gain (right)

Fig. 3. Monotone convergence of the objective (top) and constraint (bottom) function norms

Fig. 1. Adapted nonlinear internal model control scheme

TABLE I THREE MODELS USED IN NUMERICAL AND EXPERIMENTAL VALIDATION

Citations

Journal Article•10.1109/TIE.2017.2782201

Nonlinear Monotonically Convergent Iterative Learning Control for Batch Processes

Jingyi Lu, +3 more

- 01 Jul 2018

- IEEE Transactions on Industrial Electron...

TL;DR: The proposed NMC-ILC is an optimization-based control strategy, in which the original nonlinear process is linearly approximated to reduce the complexity of the optimization problem accompanied.

...read moreread less

Journal Article•10.1016/J.FLOWMEASINST.2011.11.001

A new constrained hierarchical reconstruction method for electrical capacitance tomography

Samir Teniou, +5 more

- 01 Mar 2012

- Flow Measurement and Instrumentation

TL;DR: Experimental results show that the proposed scheme improves the accuracy and the quality of the reconstructed images while keeping the computation time significantly lower than other traditional methods.

...read moreread less

Journal Article•10.4028/WWW.SCIENTIFIC.NET/KEM.460-461.184

Moving Horizon Model Inversion for Nonlinear ILC

Gang Xu, +3 more

- 01 Jan 2011

- Key Engineering Materials

TL;DR: The model inversion approach can be used in iterative learning control to refine the input to make the response of the system match the reference trajectory as close as possible.

...read moreread less

Book Chapter•10.1007/978-3-642-25553-3_31

Attitude Measurement of Dynamic Object by Laser Tracking System and Optoelectronic Methodology

Minghua Pan, +2 more

- 01 Jan 2011

TL;DR: This paper presents a low-cost laser tracking system to measure attitudes of dynamic object and position of the moving target which only need one feature point fixed on it with a Gauss-Newton iterative method.

...read moreread less

•Dissertation

Development of a Simulation based Powertrain Design Framework for Evaluation of Transient Soot Emissions from Diesel Engine Vehicles.

Rahul Ahlawat

- 01 Jan 2011

TL;DR: Ahlawat et al. as discussed by the authors developed a modeling and simulation framework for diesel engine vehicles to enable transient soot emissions as a constraint in powertrain design and control, which significantly improves over the steady-state maps, while the model remains computationally efficient for systemslevel work.

...read moreread less

References

Journal Article•10.1109/MCS.2006.1636313

A survey of iterative learning control

Douglas A. Bristow, +2 more

- 30 May 2006

- IEEE Control Systems Magazine

TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.

...read moreread less

•Book

System Identification: A Frequency Domain Approach

Rik Pintelon, +1 more

- 31 Dec 2003

TL;DR: Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach.

...read moreread less

2.8K

Journal Article•10.1016/S0165-1684(00)00231-0

A bibliography on nonlinear system identification

Georgios B. Giannakis, +1 more

- 01 Mar 2001

- Signal Processing

TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.

...read moreread less

259

•Journal Article•10.1103/PHYSREVA.45.5524

Fitting ordinary differential equations to chaotic data.

Ellen Baake, +3 more

- 15 Apr 1992

- Physical Review A

TL;DR: It is claimed that the problem of estimating parameters in systems of ordinary differential equations which give rise to chaotic time series is naturally tackled by boundary value problem methods and Lyapunov exponents can be computed accurately from time series much shorter than those required by previous methods.

...read moreread less

227

Journal Article•10.1016/S0967-0661(02)00103-X

Off-line feed-forward and feedback control on a vibration rig

Joris De Cuyper, +2 more

- 01 Feb 2003

- Control Engineering Practice

TL;DR: In this article, the authors discuss the improvement of the tracking accuracy on an automotive test rig by extending the industrial available off-line controller with an H ∞ feedback controller, which is designed based on a mixed sensitivity approach.

...read moreread less

Model based nonlinear iterative learning control: A constrained Gauss-Newton approach

Chat with Paper

AI Agents for this Paper

Most frequently asked questions

1. What are the contributions in "Model based nonlinear iterative learning control: a constrained gauss-newton approach" ?

Figures

Citations

Nonlinear Monotonically Convergent Iterative Learning Control for Batch Processes

A new constrained hierarchical reconstruction method for electrical capacitance tomography

Moving Horizon Model Inversion for Nonlinear ILC

Attitude Measurement of Dynamic Object by Laser Tracking System and Optoelectronic Methodology

Development of a Simulation based Powertrain Design Framework for Evaluation of Transient Soot Emissions from Diesel Engine Vehicles.

References

A survey of iterative learning control

System Identification: A Frequency Domain Approach

A bibliography on nonlinear system identification

Fitting ordinary differential equations to chaotic data.

Off-line feed-forward and feedback control on a vibration rig

Related Papers (5)

An iterative method for the identification of nonlinear systems using a Uryson model

Minimum-energy approach to stable inversion of nonminimum phase systems

Method of Parametric Optimization of Nonlinear Continuous Systems of Joint Estimation and Control

Neural Dynamics and Newton–Raphson Iteration for Nonlinear Optimization

Min-plus techniques for set-valued state estimation