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1

Power quality enhancement in double fed induction generator using iterative learning control

Djaidja Oussama, Mekki Hemza, Zeghleche Samir, Djerioui Ali

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

41--48

EN

This paper presents a combination of the proposed Sliding Mode Control and a newly developed iterative learning control technique for harmonic compensation for the fault’s effect to adjust the active and reactive power to their desired references. The classical SMC cannot deal with the effect of the faults that can achieve graceful system degradation. Indeed, when there are significant disturbances, the input control signal of the SM controller is gradually adjusted by the ILC harmonic compensator in order to reject the disruptive harmonics effectively. Simulation results are given to demonstrate the effectiveness of the suggested SMC-ILC in terms of active and reactive power responses. The obtained results illustrate that the SMC-ILC strategy is valid and capable of ensuring a ripple-free operation in the presence of faults. The proposed controller is characterized by its simple design, robustness, and efficiency, which are convincing for practical application and may be used as a solution to the current Fault Tolerant Control.

PL

W artykule przedstawiono kombinację proponowanej regulacji trybu ślizgowego i nowo opracowanej techniki iteracyjnego sterowania z uczeniem w celu kompensacji harmonicznych w obecności zwarć, aby sterować mocą czynną i bierną zgodnie z ich pożądanymi wartościami odniesienia. Klasyczny SMC nie radzi sobie ze skutkami usterek, które mogą doprowadzić do płynnej degradacji systemu. Rzeczywiście, gdy występują znaczne zakłócenia, wejściowy sygnał sterujący kontrolera SM jest stopniowo regulowany przez kompensator harmonicznych ILC w celu skutecznego odrzucenia zakłócających harmonicznych. Przedstawiono wyniki symulacji, aby pokazać skuteczność proponowanego SMC-ILC w zakresie odpowiedzi mocy czynnej i biernej. Uzyskane wyniki pokazują, że strategia SMC-ILC jest poprawna i zdolna do zapewnienia działania bez tętnień w przypadku wystąpienia usterki. Proponowany sterownik charakteryzuje się wytrzymałością, wydajnością i prostą konstrukcją, które przekonują do praktycznego zastosowania i mogą być stosowane jako alternatywa dla dotychczasowych Kontrola odporna na awarie.

2

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.

3

Application of fractional calculus in iterative sliding mode synchronization control

Zhang Xin, Wen-Ru Lu, Zhang Liang, Xu Wen-Bo

Archives of Electrical Engineering

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2020

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Vol. 69, nr 3

499--519

EN

In order to control joints of manipulators with high precision, a position tracking control strategy combining fractional calculus with iterative learning control and sliding mode control is proposed for the control of a single joint of manipulators. Considering the coupling between joints of manipulators, a fractional-order iterative sliding mode crosscoupling control strategy is proposed and the theoretical proof of its progressive stability is given. The paper takes a two-joint manipulator as the research object to verify the control strategy of a single-joint manipulator. The results show that the control strategy proposed in this paper makes the two-joint mechanical arm chatter less and the tracking more accurate. The synchronous control of the manipulator is verified by a three-joint manipulator. The results show that the angular displacement adjustment times of the threejoint manipulator are 0.11 s, 0.31 s and 0.24 s, respectively. 3.25 s > 5 s, 3.15 s of a PD cross-coupling control strategy; 2.85 s, 2.32 s, 4.22 s of a PD iterative cross-coupling control strategy; 0.14 s, 0.33 s, 0.28 s of a fractional-order sliding mode cross-coupling control strategy. The root mean square error of the position error of the designed control strategy is 6.47 × 10−6 rad, 3.69 × 10−4 rad, 6.91 × 10−3 rad, respectively. The root mean square error of the synchronization error is 3.96×10−4 rad, 1.36×10−3 rad, 7.81×10−3 rad, superior to the other three control strategies. The results illustrate the effectiveness of the proposed control method.

4

Constrained Output Iterative Learning Control

Yovchev Kaloyan, Delchev Kamen, Krastev Evgeniy

Archives of Control Sciences

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2020

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Vol. 30, No. 1

157--176

EN

Iterative Learning Control (ILC) is a well-known method for control of systems performing repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for non-linear state space constrained systems. In the existing literature there is no general solution for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm (BEA) and resolves the transient growth error problem, which is a major obstacle in applying ILC to non-linear systems. Another advantage of COILC is that this method can be applied to constrained output systems. Unlike other ILC methods the COILC method employs an algorithm that stops the iteration before the occurrence of a violation in any of the state space constraints. This way COILC resolves both the hard constraints in the non-linear state space and the transient growth problem. The convergence of the proposed numerical procedure is proved in this paper. The performance of the method is evaluated through a computer simulation and the obtained results are compared to the BEA method for controlling non-linear systems. The numerical experiments demonstrate that COILC is more computationally effective and provides better overall performance. The robustness and convergence of the method make it suitable for solving constrained state space problems of non-linear systems in robotics.

5

Application of iterative learning control for ripple torque compensation in PMSM drive

Wójcik Adrian, Pajchrowski Tomasz

Archives of Electrical Engineering

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2019

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Vol. 68, nr 2

309--324

EN

The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time- consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.

6

A double-iterative learning and cross-coupling control design for high-precision motion control

Xu Wan, Hou Jie, Yang Wei, Wang Cong

Archives of Electrical Engineering

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2019

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Vol. 68, nr 2

427--442

EN

In multi-axis motion control systems, the tracking errors of single axis load and the contour errors caused by the mismatch of dynamic characteristics between the moving axes will affect the accuracy of the motion control system. To solve this issue, a biaxial motion control strategy based on double-iterative learning and cross-coupling control is proposed. The proposed control method improves the accuracy of the motion control system by improving individual axis tracking performance and contour tracking performance. On this basis, a rapid control prototype (RCP) is designed, and the experiment is verified by the hardware and software platforms, LabVIEW and Compact RIO. The whole design shows enhancement in the precision of the motion control of the multi- axis system. The performance in individual axis tracking and contour tracking is greatly improved.

7

Ufnalski B., Grzesiak L M., Kaszewski A., Galecki A.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 5

38--46

EN

There are two main techniques to solve the reference tracking problem for repetitive references and under repetitive disturbances, namely multiresonant (a.k.a. multioscillatory) controllers and iterative learning controllers. Nevertheless, neither of the approaches is a deﬁnitive winner, which is to be demonstrated herein. Both have their strengths, weaknesses and challenges. A grid-tie converter will be the case study here. The goal is to draw or inject sinusoidal currents under distorted grid voltage conditions. The supporting feedforward controller will be addressed within the context of the discussed repetitive control task. The case will be illustrated using numerical simulations. Our main goal is to make practitioners familiar with the relationships between these two control methods.

PL

Istnieją dwia główne sposoby rozwiązywania zadania regulacji nadążnej dla powtarzalnego sygnału zadanego w obecności powtarzalnego zakłócenia, jest to zastosowanie regulatorów wielorezonansowych (zwanych też wielooscylacyjnymi) oraz regulatorów z uczeniem iteracyjnym. Jednak żadnego z tych rozwiązań nie można uznać za jednoznacznie lepsze, co zostanie tutaj pokazane. Oba cechują zarówno mocne strony, jak i pewne słabci oraz wyzwania implementacyjne. Przekształtnik sieciowy posłuży tutaj za przykład. Celem jest pobieranie lub oddawanie sinusoidalnego prądu sieci pomimo odkształconego napięcia. Omówione zostanie również sprzężenie w przód od zakłócenia w kontekście zadania sterowania powtarzalnego. Zagadnienie zostanie zilustrowane przy użyciu symulacji komputerowych. Naszym głównym celem jest pokazanie praktykom związków pomiędzy tymi dwiema metodami sterowania.

8

A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

Wang M., Bian G., Li H.

Archives of Control Sciences

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2016

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Vol. 26, no. 3

297--310

EN

This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

9

A plug-in direct particle swarm repetitive controller for a single-phase inverter

Ufnalski B., Grzesiak L M.

Przegląd Elektrotechniczny

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2014

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R. 90, nr 6

6--11

EN

The paper presents an online particle swarm optimizer (PSO) as an iterative learning controller for the single phase inverter with an output LC filter. The novelty of the solution lies in the fact that the swarm directly stores samples of the control signal. The swarm optimizes, according to a user-defined performance index, in online mode the control signal to reject the repetitive disturbance (the load current drawn, for example, by the diode rectifier). The concept of the direct swarm controller is investigated with the help of numerical simulations.

PL

W artykule opisano regulator rojowy realizujący sterowanie z uczeniem iteracyjnym dla jednofazowego falownika napięcia z wyjściowym filtrem LC. Oryginalność rozwiazania polega na fakcie bezpośredniego przechowywania próbek sygnału sterujacego przez rój cząstek. Rój optymalizuje w trybie on-line sygnał sterujący eliminując wpływ okresowego zakłócenia (prądu obciążenia pobieranego, na przykład, przez prostownik diodowy z filtrem pojemnościowym) na jakość napięcia wyjściowego. Sygnał sterujący jest optymalny z uwagi na zdefiniowany przez użytkownika wskaźnik jakości. Koncepcja bezpośredniego regulatora rojowego została zbadana przy użyciu technik modelowania numerycznego.

10

Iterative learning control with sampled-data feedback for robot manipulators

Delchev K., Boiadjiev G., Kawasaki H., Mouri T.

Archives of Control Sciences

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2014

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Vol. 24, no. 3

299--319

EN

This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.

11

Artificial neural network based voltage controller for the single phase true sine wave inverter – a repetitive control approach

Ufnalski B., Grzesiak L.M.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 4

14-18

EN

The paper presents novel error backpropagation based neurocontroller for true sine wave inverter. The controller is trained in on-line mode. Adaptation algorithm takes into account repetitiveness of the process to be controlled. The cost function evaluates performance of the controller over the whole period of the reference signal and the weights are updated only once a period of this signal. A model-free concept is employed and hence no neural (or of any other type) model of the plant is needed. Proposed topology does not limit its area of implementation to the discussed converter. The controller is capable to maintain a high-quality output voltage waveform in the presence of periodic disturbance caused by nonlinear loads.

PL

W artykule przedstawiono metodę budowy neuronowego regulatora napięcia dla falownika o sinusoidalnym napięciu wyjściowym. Regulator uczony jest w trybie on-line. Algorytm adaptacji wag sieci uwzględnia powtarzalność procesu poprzez odpowiednią definicję funkcji celu oraz uaktualnianie wag sieci raz na okres sygnału zadanego. Synteza układu regulacji nie wymaga identyfikowania modelu obiektu (podejście typu model-free). Zaproponowana topologia regulatora umożliwia jego wykorzystanie również do sterowania innymi procesami powtarzalnymi. Regulator pozwala na utrzymanie wysokiej jakości napięcia wyjściowego również dla okresowych obciążeń nieliniowych.

12

A new procedure for the design of iterative learning controllers using a 2D systems formulation of processes with uncertain spatio-temporal dynamics

Cichy B., Gałkowski K., Dąbkowski K., Aschemann H., Rauh A.

Control and Cybernetics

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2013

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Vol. 42, no. 1

9--26

EN

Iterative Learning Control (ILC) is well established in control of linear and nonlinear dynamic systems, both as to underlying theory and experimental validation. This approach specifically aims at applications with the same operation repeated over finite time intervals and reset taking place between subsequent executions (the trials). The main principle behind ILC is to suitably use information from previous trials in selection of the input signal in the current trial with the objective of performance improvement from trial to trial. In this paper, new computationally efficient results are presented for an extension of the ILC approach to the uncertain 2D systems that arise from time and space discretization of partial differential equations. This type of application implies the need to use a spatio–temporal setting for the analysis of the control procedure. The resulting control laws can be computed using Linear Matrix Inequalities (LMIs). An illustrative example is provided.

13

Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform

Ufnalski B., Grzesiak L. M., Gałkowski K.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2013

|

Vol. 61, nr 3

649--660

EN

This paper presents the application of a particle swarm optimization (PSO) to determine iterative learning control (ILC) law gains for an inverter with an LC output filter. Available analytical tuning methods derived for a given type of ILC law are not very straightforward if additional performance requirements of the closed-loop system have to be met. These requirements usually concern the dynamics of a response to a reference signal, the dynamics of a disturbance rejection, the immunity against expected level of system and measurement noise, the robustness to anticipated variations of parameters, etc. An evolutionary optimization approach based on the swarm intelligence is proposed here. It is shown that in the case of the ILC applied to the LC filter, a cost function based on mean squares can produce satisfactory tuning effects. The efficacy of the procedure is illustrated by performing the optimization for various noise levels and various requested dynamics.

14

Projektowanie schematów iteracyjnego sterowania z nauczaniem z użyciem teorii procesów powtarzalnych

Paszke W.

Prace Naukowe Politechniki Warszawskiej. Elektronika

|

2012

|

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.

15

Simulation-based design of monotonically convergent iterative learning control for nonlinear systems

Delchev K.

Archives of Control Sciences

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2012

|

Vol. 22, no. 4

467-480

EN

This paper deals with a simulation-based design of model-based iterative learning control (ILC) for multi-input, multi-output nonlinear time-varying systems. The main problem of the implementation of the nonlinear ILC in practice is possible inadmissible transient growth of the tracking error due to a non-monotonic convergence of the learning process. A model-based nonlinear closed-loop iterative learning control for robot manipulators is synthesized and its tuning depends on only four positive gains of both controllers - the feedback one and the learning one. A simulation-based approach for tuning the learning and feedback controllers is proposed to achieve fast and monotonic convergence of the presented ILC. In the case of excessive growth of transient errors this approach is the only way for learning gains tuning by using classical engineering techniques for practical online tuning of feedback gains.

16

Design of iterative learning control for simple robot based on neural network robot model

Lesewed A. A., Kurek J.

Pomiary Automatyka Kontrola

|

2009

|

R. 55, nr 3

205-208

EN

Design of the iterative learning control (TLC) for robot manipulator with 2 degree of freedom based on model of the robot approximated by neural network is presented. The robot model has form of the Lagrange-Euler equation and neural network was trained to estimate the model parameters. Then, the estimated model was used for synthesis of ILC.

PL

W pracy przedstawiono syntezy iteracyjnie uczącego się sterowania dla robota o 2 stopniach swobody na podstawie modelu aproksymowanego przy pomocy sieci neuronowych. Model robota ma formę równań Lagrange'a-Eulera, którego nieliniowe funkcje zostały wyznaczone przez odpowiednio wytrenowaną sieć neuronową. Aproksymowany model został następnie wykorzystany do syntezy regulatora.

17

Iterative learning control for robot manipulators

Bouakrif F., Boukhetala D., Boudjema F.

Archives of Control Sciences

|

2007

|

Vol. 17, no. 1

57-69

EN

In this paper, we present a time-domain iterative learning control scheme for the trajectory tracking problem of rigid robot manipulators that perform repeated tasks. The proposed control scheme comprises a computed torque control designed exploiting the approximated linear model of a manipulator and a learning law to compensate effects of nonlinear terms, that are ignored in obtaining the linear model, and the external disturbance. We show that the iterative learning controller is capable of effectively canceling the disturbances caused by nonlinear terms and other disturbance. The asymptotic stability of the closed-loop system is guaranteed, and the conditions of this stability are given. Simulation results on PUMA 560 robot show clearly efficiency of the proposed scheme.

18

Predication of violations in road transportation system

Chaali-Djelassi A., Vanderhaegen F.

Journal of KONBiN

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2006

|

No. 2

57--68

EN

Risk analysis in Human-Machine System has to take into account intentional Human Errors in order to reduce their occurrences and/or their consequences. After an introduction of the barrier removal concept and the BCD model, the article presents a comparative prediction study between a first human behaviour prediction method based on the barrier removal utility and the Iterative Learning Control and a second one based on the BCD model and the Artificial Neural Networks. Its interest is illustrated by the presentation of the results of an experimental study realized with a car driving simulator.

19

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

|

2003

|

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.

20

On the Interaction Between Theory, Experiments, and Simulation in Developing Practical Learning Control Algorithms

Longman R. W.

International Journal of Applied Mathematics and Computer Science

|

2003

|

Vol. 13, no 1

101-111

EN

Iterative learning control (ILC) develops controllers that iteratively adjust the command to a feedback control system in order to converge to zero tracking error following a specific desired trajectory. Unlike optimal control and other control methods, the iterations are made using the real world in place of a computer model. If desired, the learning process can be conducted both in the time domain during each iteration and in repetitions, making ILC a 2D system. Because ILC iterates with the real world, and aims for zero error, the field pushes the limits of theory, modeling, and simulation, to predict the behavior when applied in the real world. It is the thesis of this paper that in order to make significant progress in this field it is essential that the research effort employ a coordinated simultaneous synergistic effort involving theory, experiments, and serious simulations. Otherwise, one very easily expends effort on something that seems fundamental from the theoretical perspective, but in fact has very little relevance to the performance in real world applications.