Temat: iterative learning - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: A double-iterative learning and cross-coupling control design for high-precision motion control
Autorzy:: Xu, Wan
Hou, Jie
Yang, Wei
Wang, Cong
Tematy:: iterative learning control
cross-coupled control
contour tracking performance
double-iterative learning and cross coupling; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/140531.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: On the Interaction Between Theory, Experiments, and Simulation in Developing Practical Learning Control Algorithms
Autorzy:: Longman, R. W.
Tematy:: automatyka
robotyka
iterative learning control
ILC
2D systems
learning transients; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/908249.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A new procedure for the design of iterative learning controllers using a 2D systems formulation of processes with uncertain spatio-temporal dynamics
Autorzy:: Cichy, B.
Gałkowski, K.
Dąbkowski, K.
Aschemann, H.
Rauh, A.
Tematy:: iterative learning control
spatio-temporal dynamics
Crank-Nicolson discretization; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/205941.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Constrained Output Iterative Learning Control
Autorzy:: Yovchev, Kaloyan
Delchev, Kamen
Krastev, Evgeniy
Tematy:: constrained output systems
convergence analysis
iterative learning control
robot manipulators; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/229181.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Iterative learning control with sampled-data feedback for robot manipulators
Autorzy:: Delchev, K.
Boiadjiev, G.
Kawasaki, H.
Mouri, T.
Tematy:: sampled-data systems
iterative learning control
robot manipulators
convergence analysis; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/229323.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Comparison of the Stability Boundary and the Frequency Response Stability Condition in Learning and Repetitive Control
Autorzy:: Songschon, S.
Longman, R. W.
Tematy:: automatyka
robotyka
iterative learning control
repetitive control
stability
monotonic convergence; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/908202.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform
Autorzy:: Ufnalski, B.
Grzesiak, L. M.
Gałkowski, K.
Tematy:: iterative learning control
sine wave inverter
particle swarm optimization (PSO); Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/200271.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems
Autorzy:: Delchev, K.
Tematy:: simulation-based design
iterative learning control
nonlinear dynamic systems
learning controller
feedback controller; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/229340.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator
Autorzy:: Wang, M.
Bian, G.
Li, H.
Tematy:: iterative learning control
fuzzy control
fuzzy gain adjustment
single joint manipulator; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/229401.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Application of iterative learning control for ripple torque compensation in PMSM drive
Autorzy:: Wójcik, Adrian
Pajchrowski, Tomasz
Tematy:: ripple torque
iterative learning control
artificial neural network
permanent magnet synchronous motor; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/140797.pdf Link otwiera się w nowym oknie
Opis:: 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.
Dostawca treści:: Biblioteka Nauki

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