Iterative learning control for a class of nonlinear systems

Ahn, Hyun-Sik; Choi, Chong‐Ho; Kim, Kwang-Bae

doi:10.1016/0005-1098(93)90024-n

Cited by 108 publications

(60 citation statements)

References 5 publications

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“…Most existing ILCs are of either D-type or P-type or their variations (Hauser 1987, Bien and Huh 1989, Arimoto 1990, Heinzinger et al 1992, Kuc et al 1992, Moore et al 1992, Ahn et al 1993, Saab 1994, Chien and Liu 1996, Cheah and Wang 1998b, Wang and Cheah 1998, Xu 1998, Xu and Zhu 1999. Here we revisit these two controllers for comparisons and to motivate the proposed anticipatory ILC approach.…”

Section: Revisiting D-type and P-type Ilcmentioning

confidence: 99%

On D-type and P-type ILC designs and anticipatory approach

Wang

2000

International Journal of Control

115

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Many schemes of iterative learning control (ILC) have been developed for continuous-time, non-linear dynamic systems to improve tracking performance. Two schemes, D-type and P-type, have been the bases for many ILC designs. Recently, the anticipatory ILC scheme has been introduced, on the basis of a di erent approach (Wang 1998a(Wang , 1999. In this paper, these basic schemes are compared from both analysis and implementation view points. The anticipatory ILC scheme is designed on the basis of a causal pair of the action taken and its resulting state variables. This approach has the anticipatory characteristics of the D-type ILC and the simplicity for implementation of P-type ILCs. A sampled-data ILC scheme is presented as another form of this anticipatory ILC scheme. Furthermore, control device saturation is taken into account and tracking error convergence results are established, with proofs. The convergence results are also provided in the presence of uncertainties, disturbances and measurement noises. Experimental results are presented to show the e ectiveness of this scheme.

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Section: Revisiting D-type and P-type Ilcmentioning

confidence: 99%

On D-type and P-type ILC designs and anticipatory approach

Wang

2000

International Journal of Control

115

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“…Examples of such work include the use of more than one previous cycle and also higher derivatives as seen in [3,4,5]. The former increases the robustness, as defined by these authors, at the price of convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of iterative learning control algorithms for non-minimum phase plants

Freeman

Lewin

Rogers

2005

International Journal of Control

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The purpose of this paper is two-fold, firstly it describes the development and modelling of an experimental test facility as a platform on which to assess the performance of Iterative Learning Control (ILC) schemes. This facility includes a non-minimum phase component. Secondly, P-Type, D-Type and phase-lead types of the algorithm have been implemented on the test-bed, results are presented for each method and their performance is compared. Although all the ILC strategies tested experience eventual divergence when applied to a non-minimum phase system, it is found that there is an optimum phase-lead ILC design that maximizes convergence and minimizes error. A general method of arriving at this phase-lead from knowledge of the plant model is described. A 1 variety of filters have been applied and assessed in order to improve the overall performance of the algorithm.

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“…Several ILC schemes for nonlinear systems have been proposed in the literature (see, for instance, the following non-exhaustive list of references and the references therein [1], [6], [7], [8], [10], [12], [17], [18], [19], [20], [21], [23], [24], [25]). They are generally based upon 1) the global Lipschitz condition assumption and the use of the λ-norm, or 2) the use of some structural assumptions as well as a partial knowledge of the system dynamics, restricting, thereby, the class of systems considered, or 3) the description of a given nonlinear system by a set of blended linear models and a validity function providing a time-varying weight (or probability) for each model according to the region of operation of the nonlinear system [19].…”

Section: Introductionmentioning

confidence: 99%

Cascaded Iterative Learning Control for a Class of Uncertain Time-Varying Nonlinear Systems

Tayebi

Polushin

Chien

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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Abstract-We propose a simple recursive iterative learning control (ILC) scheme for a class of uncertain time-varying nonlinear systems. The proposed constructive backsteppinglike procedure provides, in a systematic way, a control Lyapunov-like function leading to a simple recursive ILC scheme guaranteeing the existence of a finite time-interval on which a perfect tracking can be achieved when the number of iteration goes to infinity. Simulation results are also provided to show the effectiveness of the proposed ILC scheme.

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Iterative learning control for a class of nonlinear systems

Cited by 108 publications

References 5 publications

On D-type and P-type ILC designs and anticipatory approach

On D-type and P-type ILC designs and anticipatory approach

Experimental evaluation of iterative learning control algorithms for non-minimum phase plants

Cascaded Iterative Learning Control for a Class of Uncertain Time-Varying Nonlinear Systems

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