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Tytuł:
A double-iterative learning and cross-coupling control design for high-precision motion control
Autorzy:
Xu, Wan
Hou, Jie
Yang, Wei
Wang, Cong
Powiązania:
https://bibliotekanauki.pl/articles/140531.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
iterative learning control
cross-coupled control
contour tracking performance
double-iterative learning and cross coupling
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.
Źródło:
Archives of Electrical Engineering; 2019, 68, 2; 427-442
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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.
Powiązania:
https://bibliotekanauki.pl/articles/205941.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
iterative learning control
spatio-temporal dynamics
Crank-Nicolson discretization
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.
Źródło:
Control and Cybernetics; 2013, 42, 1; 9-26
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the Interaction Between Theory, Experiments, and Simulation in Developing Practical Learning Control Algorithms
Autorzy:
Longman, R. W.
Powiązania:
https://bibliotekanauki.pl/articles/908249.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
robotyka
iterative learning control
ILC
2D systems
learning transients
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 1; 101-111
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of the Stability Boundary and the Frequency Response Stability Condition in Learning and Repetitive Control
Autorzy:
Songschon, S.
Longman, R. W.
Powiązania:
https://bibliotekanauki.pl/articles/908202.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
robotyka
iterative learning control
repetitive control
stability
monotonic convergence
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 2; 169-177
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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.
Powiązania:
https://bibliotekanauki.pl/articles/200271.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
iterative learning control
sine wave inverter
particle swarm optimization (PSO)
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.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 649-660
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Constrained Output Iterative Learning Control
Autorzy:
Yovchev, Kaloyan
Delchev, Kamen
Krastev, Evgeniy
Powiązania:
https://bibliotekanauki.pl/articles/229181.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
constrained output systems
convergence analysis
iterative learning control
robot manipulators
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.
Źródło:
Archives of Control Sciences; 2020, 30, 1; 157-176
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Iterative learning control with sampled-data feedback for robot manipulators
Autorzy:
Delchev, K.
Boiadjiev, G.
Kawasaki, H.
Mouri, T.
Powiązania:
https://bibliotekanauki.pl/articles/229323.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
sampled-data systems
iterative learning control
robot manipulators
convergence analysis
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.
Źródło:
Archives of Control Sciences; 2014, 24, 3; 299-319
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator
Autorzy:
Wang, M.
Bian, G.
Li, H.
Powiązania:
https://bibliotekanauki.pl/articles/229401.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
iterative learning control
fuzzy control
fuzzy gain adjustment
single joint manipulator
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.
Źródło:
Archives of Control Sciences; 2016, 26, 3; 297-310
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simulation-based design of monotonically convergent iterative learning control for nonlinear systems
Autorzy:
Delchev, K.
Powiązania:
https://bibliotekanauki.pl/articles/229340.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
simulation-based design
iterative learning control
nonlinear dynamic systems
learning controller
feedback controller
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.
Źródło:
Archives of Control Sciences; 2012, 22, 4; 467-480
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of iterative learning control for ripple torque compensation in PMSM drive
Autorzy:
Wójcik, Adrian
Pajchrowski, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/140797.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ripple torque
iterative learning control
artificial neural network
permanent magnet synchronous motor
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.
Źródło:
Archives of Electrical Engineering; 2019, 68, 2; 309-324
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
System Identification from Multiple-Trial Data Corrupted by Non-Repeating Periodic Disturbances
Autorzy:
Phan, M. Q.
Longman, R. W.
Lee, S. C.
Lee, J. W.
Powiązania:
https://bibliotekanauki.pl/articles/908209.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
robotyka
system identification
disturbance identification
iterative learning control
repetitive control
interaction matrix
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 2; 185-192
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Iterative Learning Control for Over-Determined, Under-Determined, and Ill-Conditioned Systems
Autorzy:
Avrachenkov, K. E.
Longman, R. W.
Powiązania:
https://bibliotekanauki.pl/articles/908247.pdf
Data publikacji:
2003
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automatyka
robotyka
iterative learning control
over-determined
under-determined
ill-conditioned systems
pseudoinverse
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2003, 13, 1; 113-122
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of fractional calculus in iterative sliding mode synchronization control
Autorzy:
Zhang, Xin
Wen-Ru, Lu
Zhang, Liang
Xu, Wen-Bo
Powiązania:
https://bibliotekanauki.pl/articles/141629.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
cross-coupling control
fractional calculus
iterative learning control
PD control
robot arm
sliding mode control
Opis:
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.
Źródło:
Archives of Electrical Engineering; 2020, 69, 3; 499-519
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Design of iterative learning control for simple robot based on neural network robot model
Synteza iteracyjnie uczqcego siq sterowania prostego robota na podstawie neuronowego modelu robota
Autorzy:
Lesewed, A. A.
Kurek, J.
Powiązania:
https://bibliotekanauki.pl/articles/154502.pdf
Data publikacji:
2009
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
model neuronowy robota przemyslowego
iteracyjnie uczące się sterowanie
neural model of industrial robot
iterative learning control
Opis:
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.
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.
Źródło:
Pomiary Automatyka Kontrola; 2009, R. 55, nr 3, 3; 205-208
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Iterative learning fault-tolerant control for the networked control systems with initial state disturbance
Autorzy:
Xingjian, Fu
Qianjun, Zhao
Powiązania:
https://bibliotekanauki.pl/articles/2173682.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
networked control systems
initial state disturbance
iterative learning
actuator failure
fault-tolerant control
sieciowe systemy sterowania
zakłócenie stanu początkowego
uczenie iteracyjne
awaria siłownika
kontrola odporna na awarie
Opis:
The iterative learning fault-tolerant control strategies with non-strict repetitive initial state disturbances are studied for the linear discrete networked control systems (NCSs) and the nonlinear discrete NCSs. In order to reduce the influence of the initial state disturbance in iteration, for the linear NCSs, considering the external disturbance and actuator failure, the iterative learning fault-tolerant control strategy with impulse function is proposed. For the nonlinear NCSs, the external disturbance, packet loss and actuator failure are considered, the iterative learning fault-tolerant control strategy with random Bernoulli sequence is provided. Finally, the proposed control strategies are used for simulation research for the linear NCSs and the nonlinear NCSs. The results show that both strategies can reduce the influence of the initial state disturbance on the tracking effect, which verifies the effectiveness of the given method.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e140934
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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