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1

A new improved control for power quality enhancement in double fed induction generator using iterative learning control

Djaidja Oussama, Mekki Hemza, Zeghlache Samir, Djerioui Ali

Diagnostyka

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2023

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Vol. 24, No. 3

art. no. 2023309

EN

This work presents a new Fault Tolerant Control approach for a doubly fed induction generator using Iterative Learning Control when the fault occurs. The goal of this research is to apply the proposed ILC controller in conjunction with vector control for doubly fed induction generator to enhance its reliability and availability under broken rotor bars. However, the performances of classical VC control are often characterized by their inability to deal with the effects of faults. To overcome these drawbacks, a combination of VC control and iterative learning control is described. The input control signal of the VC controller is gradually regulated by the ILC harmonic compensator in order to eliminate the faults effect. The improvement of this approach related to active and reactive power ripples overshoot and response time have been explained. Which active and reactive power response time have been reduced more than 84% and 87.5 % respectively. The active and reactive power overshoots have been reduced about 45% and 35% respectively. The obtained results emphasize the efficiency and the ability of the proposed FTC to enhance the power quality in faulty condition.

2

Projektowanie schematów iteracyjnego sterowania z nauczaniem z użyciem teorii procesów powtarzalnych

Paszke W.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2012

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z. 182, t. 2

481--490

PL

Iteracyjne sterowanie z nauczaniem (ILC) jest powszechnie stosowaną techniką regulacji automatycznej używaną w układach wykonujących powtarzalne operacje, gdzie podstawowym wymaganiem jest osiągniecie wysokiej dokładności śledzenia sygnału referencyjnego. Przykładem takich systemów są manipulatory robotyczne, które powtarzają tą samą operację wiele razy. Proces iteracyjnego sterowania ma wewnętrzną dwuwymiarową/powtarzalną strukturę ze względu na propagacje informacji w dwóch niezależnych kierunkach, tj.: w kierunku z iteracji na iteracje oraz wzdłuż aktualnie wykonywanej iteracji. Niniejszy artykuł prezentuje nową procedurę wyznaczania parametrów regulatorów opartą na modelowaniu schematu ILC jako procesu powtarzalnego i zastosowaniu teorii stabilności tych procesów. Prezentowana procedura działa również w przypadku układów, których stopień względny transmitancji jest większy od jedności. Co więcej, zaproponowaną procedurę można z łatwością rozszerzyć do przypadku występowania niepewności parametrów modelu układu. Efektywność zaprezentowanej procedury syntezy schematu ILC jest zweryfikowana z użyciem przykładu numerycznego bazującego na modelu fizycznego systemu robotycznego.

EN

Iterative learning control (ILC) can be applied to systems operating in a repetitive mode with the requirement that a given reference trajectory defined over a finite interval is followed to a high precision. Examples of such systems include robotic manipulators that are required to repeat a given task, chemical batch processes or, more generally, the class of tracking systems. Each execution is known as a trial and ILC has an inherent two-dimensional/repetitive process structure due to information propagation from trial-to-trial and along the trial respectively. In this paper, the repetitive process setting for analysis is used to develop a design algorithm which in one step synthesizes both a stabilizing feedback controller in the time domain and a feedforward (learning) controller which guarantees convergence in the trial domain. A new control law design algorithm for ILC can he applied to processes whose linear, state-space model has zero first Markov parameter. Also, relative easy extension is possible to the case where system matrices are not precisely known. Finally, the algorithms developed are applied to a multi-axis gantry robot used to validate many competing ILC designs.

3

System Identification from Multiple-Trial Data Corrupted by Non-Repeating Periodic Disturbances

Phan M. Q., Longman R. W., Lee S. C., Lee J. W.

International Journal of Applied Mathematics and Computer Science

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2003

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Vol. 13, no 2

185-192

EN

Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat. Furthermore, the system may not return to the same initial state at the end of each trial before starting the next trial. In spite of these constraints, this paper shows how to identify the system disturbance-free dynamics from disturbance-corrupted input-output data collected over multiple trials without having to measure the disturbances directly. The system disturbance-free model can then be used to identify the disturbances as well, for use in learning or repetitive control. This paper represents the first extension of the interaction matrix approach to the multiple-trial environment of iterative learning control.

4

Iterative Learning Control for Over-Determined, Under-Determined, and Ill-Conditioned Systems

Avrachenkov K. E., Longman R. W.

International Journal of Applied Mathematics and Computer Science

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2003

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Vol. 13, no 1

113-122

EN

This paper studies iterative learning control (ILC) for under-determined and over-determined systems, i.e., systems for which the control action to produce the desired output is not unique, or for which exact tracking of the desired trajectory is not feasible. For both cases we recommend the use of the pseudoinverse or its approximation as a learning operator. The Tikhonov regularization technique is discussed for computing the pseudoinverse to handle numerical instability. It is shown that for over-determined systems, the minimum error is never reached by a repetition invariant learning controller unless one knows the system exactly. For discrete time uniquely determined systems it is indicated that the inverse is usually ill-conditioned, and hence an approximate inverse based on a pseudoinverse is appropriate, treating the system as over-determined. Using the structure of the system matrix, an enhanced Tikhonov regularization technique is developed which converges to zero tracking error. It is shown that the Tikhonov regularization is a form of linear quadratic ILC, and that the regularization approach solves the important practical problem of how to intelligently pick the weighting matrices in the quadratic cost. It is also shown how to use a modification of the Tikhonov-based quadratic cost in order to produce a frequency cutoff. This robustifies good learning transients, by reformulating the problem as an over-determined system.

5

Comparison of the Stability Boundary and the Frequency Response Stability Condition in Learning and Repetitive Control

Songschon S., Longman R. W.

International Journal of Applied Mathematics and Computer Science

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2003

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Vol. 13, no 2

169-177

EN

In iterative learning control (ILC) and in repetitive control (RC) one is interested in convergence to zero tracking error as the repetitions of the command or the periods in the command progress. A condition based on steady state frequency response modeling is often used, but it does not represent the true stability boundary for convergence. In this paper we show how this useful condition differs from the true stability boundary in ILC and RC, and show that in applications of RC the distinction between these conditions is of no practical significance. In ILC satisfying this frequency condition is important for good learning transients, even though the true stability boundary is very different.