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About 10,000 papers for “Iterative Learning Control Systems”

Iterative learning control for nonlinear systems

Chenglin Wen · DR-NTU (Nanyang Technological University) · 2008

In this thesis, the review of Iterative Learning Control (ILC) is first introduced, including generic description of ILC, some ILC algorithms and results and connections to other control paradigms. In a separated section, the convergence and robustness, a mainly focused problem in almost every ILC paper, is discussed. Especially, in chapter 3, we review some previous ILC work for nonlinear systems. Following this chapter, from chapter 4 to 7, we present the main contents of this thesis, we propose an iterative learning control method based on the analysis of the constraint to achieve precise tracking control of a class of constrained nonlinear systems over a finite time interval in chapter 4. The learning is done in a feedback configuration formed by the constraint, and the learning law updates the reference input from the plant input of the previous trial. A sufficient condition which guarantees the convergence of the learning is given. From the derivations, we find that the bounds of the asymptotic errors are influenced by the constraint terms and their Lie derivative with respect to (f,G,k).

Cited by 0

Iterative learning control of crystallisation systems

Nahid Sanzida · Loughborough University Institutional Repository (Loughborough University) · 2014

A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.

Cited by 0Open access

Iterative Learning Identification/Iterative Learning Control for Linear Time-Varying Systems

Nanjun Liu, Andrew G. Alleyne · Journal of Dynamic Systems Measurement and Control · 2016

This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.

Cited by 11

Iterative Learning Control of Multi-agent Systems

Pavel Pakshin, Julia Emelianova · 2017

The paper considers a group of systems (agents), each of which described by a model consisting from a linear part and a static nonlinearity, satisfying special quadratic constraints, connected by a feedback. All systems operate in the repetitive mode with a constant pass length, and with resetting to the initial state after each pass is complete. Data connections among the systems are defined by a directed graph. The problem of reaching a consensus is formulated as designing an iterative learning control law (protocol) under which the output variable of each agent reaches a reference trajectory (pass profile) as the number of passes grows infinitely. The ultimate results are written in form of linear matrix inequalities. In linear case the consensus problem is reduced to simultaneous stabilization problem. An example of networked iterative learning control design for four identical gantry robots is considered based on models constructed using measured frequency response data.

Cited by 0

Chaotic synchronizing systems via Iterative Learning Control

Chun-Kai Cheng, Paul C.-P. Chao · 2017

This study presents an application of Iterative Learning Control Theory (ILC) for synchronization between two chaotic systems with output signal error. The iterative learning law illustrates the relationship between (i-1)-th iterative error output of drive-response system and iterative (i-th) input of response system. The error convergent describes the path response system tracking the trajectory of drive system. There also proposes principles to show system synchronized is Lyapunov stable and to explain the equivalent relation of error initial conditions. The numerical simulation demonstrates the results are agreement with the proposed learning law and scheme.

Cited by 1

Iterative Learning Control for Singularly Perturbed Systems

Konstantin E. Avrachenkov, Anatoly A. Pervozvanski · 1998

The singularly perturbed systems are described by differential equations with a small parameter near higher derivatives. It turns out that most of known iterative learning control algorithms diverge in the presence of singular perturbations. The aim of the present paper is to construct learning control algorithms that are able to overcome this problem. Our approach is based on weak convergence conditions that take into account the whole range of frequency response characteristic in contrast to traditional convergence conditions based on the rough H1 norm. The first two proposed methods employ the analog and digital filtering. Whereas the learning operator of the third method approximates the inverse of the regular part of the system and at the same time appears to be a low-pass filter. The simulation of the algorithms for a benchmark model confirms all theoretical considerations. Key words: iterative learning control, singular perturbations, filtering. 1

Cited by 0

Iterative learning control for constrained linear systems

Bing Chu, D.H. Owens · International Journal of Control · 2010

This article considers iterative learning control (ILC) for linear systems with convex control input constraints. First, the constrained ILC problem is formulated in a novel successive projection framework. Then, based on this projection method, two algorithms are proposed to solve this constrained ILC problem. The results show that, when perfect tracking is possible, both algorithms can achieve perfect tracking. The two algorithms differ, however, in that one algorithm needs much less computation than the other. When perfect tracking is not possible, both algorithms can exhibit a form of practical convergence to a ‘best approximation’. The effect of weighting matrices on the performance of the algorithms is also discussed and finally, numerical simulations are given to demonstrate the effectiveness of the proposed methods.

Cited by 77

Iterative learning control for constrained nonlinear systems

Chen, Wen, Soh, Yengchai, Yin CW(尹朝万) · SIA OpenIR (Chinese Academy of Sciences) · 1999

An iterative learning control method is presented based on the analysis of the constraint to achieve precise tracking control of a class of constrained nonlinear systems over a finite time interval. The learning is done in a feedback configuration formed by the constraint, and the learning law updates the reference input from the plant input of the previous trail. A sufficient condition which guarantees the convergence of the learning is given. From the derivation, we can establish that the bounds of the asymptotic errors is due to the existence of the constraint terms and its Lie derivative with respect to (f, G, k).

Cited by 0

Robust iterative learning control for unstable MIMO systems

Lucy Hodgins, Christopher Freeman · International Journal of Control · 2025

Iterative learning control (ILC) is a well-established technique to successively improve tracking accuracy for systems that repeatedly perform the same task. Most current literature imposes constraints on the nature of the system, such as requiring it to be full-rank, or inherently stable. This paper presents a generalised ILC framework that can handle non-linear, unstable, MIMO systems with rank deficiency. This involves the minimisation of a cost function that balances tracking performance and input effort, extending previous approaches to include a 'robustness filter' within the optimisation. Gap metric analysis is then applied to examine the robustness of the resulting system, with performance bounds derived for both serial and parallel ILC architectures. A design procedure is presented that allows the designer to transparently trade-off robustness and convergence properties. The design framework is illustrated via application to the inverted pendulum problem, a classic example of a highly nonlinear, unstable, and under-actuated system.

Cited by 0Open access

On iterative learning control of nonholonomic Hamiltonian systems

Kenji Fujimoto · 2004

Abstract — This paper is concerned with iterative learning control of Hamiltonian systems with nonholonomic constraints. The author has proposed a novel iterative learning control method based on the symmetric property of Hamiltonian control systems. Unfortunately, however, it is not applicable to nonholonomic systems. The present paper proposes a set of feedback and coordinate transformations converting a given system into a form to which our method is applicable. Furthermore, an approximated version of the lerning procedure is also propsed which only requires a little knowledge of the plant system. I.

Cited by 0

Adjoints of Hamiltonian systems and iterative learning control

Kenji Fujimoto, Toshiharu Sugie · 2002

This paper is concerned with a study on the variational systems and their adjoints of Hamiltonian control systems and its application to iterative learning control, which is applicable to a class of electro-mechanical systems. First of all, the self-adjoint structure of the variational of those systems is clarified. Then a novel iterative learning control scheme is proposed based on it. This method does not require either the knowledge of physical parameters of the target system nor the time derivatives of the output signals. A concrete and effective learning algorithm for mechanical systems is also derived.

Cited by 0