Precision coordination and motion control of multiple systems via iterative learning control

Barton, Kira; Alleyne, Andrew G.

doi:10.1109/acc.2010.5531129

Cited by 9 publications

(6 citation statements)

References 87 publications

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“…The norm optimal iterative learning controller [19][20] applies norm optimization and iterative learning to the multi-axis motion control. Iterative learning control can lead to an improved tracking performance in system with a repetitive motion because the previous cycle control signal U j-1 and the previous error signal E j-1 are used to form the current control signal U j .…”

Section: A Norm Optimal Cross-coupled Iterative Learning Controlmentioning

confidence: 99%

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Hou

et al. 2020

IEEE Access

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In the process of multi-axis contour tracking control, the traditional time-invariant method could lead to a significant error in contour tracking due to the existence of two different motion conditions, namely single-axis independent motion and multi-axis coupled motion. In order to tackle this issue, a timevarying weighting matrix has been developed considering the trajectory and time-varying random disturbance. In this paper, a time-varying control method for multi-axis motion based on norm optimal cross-coupling iterative learning is proposed. Compared to the time-invariant control method, the simulation and experiment results demonstrate that the proposed method can effectively reduce the contour error improving the multi-axis control precision. INDEX TERMS Time-varying weighted matrix, Iterative learning, Norm optimal, cross coupling, Multiaxis motion control.

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Section: A Norm Optimal Cross-coupled Iterative Learning Controlmentioning

confidence: 99%

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Hou

et al. 2020

IEEE Access

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“…Though the results of [7], [8], [9], [10] are attractive, these papers have all used global information in their proposed coordination algorithms. However, recently it has been shown that formation control only using local information could be much more challenging and promising in some applications [11].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, in [8], formation flying of a set of leader-follower type satellites was ensured by repetitive learning control method. Related, in [9] a motion coordination of multiple agents was developed using iterative learning control and in [10], a group of n agents that repeatedly perform the same task was studied and it was found that by using ILC the joint work performance among the agents was improved.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent coordination by iterative learning control: Centralized and decentralized strategies

Ahn

Chen

Moore

2011

2011 IEEE International Symposium on Intelligent Control

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Iterative learning control (ILC), an approach to achieve perfect trajectory tracking for uncertain dynamic systems that are periodic or repetitive, can be viewed as a kind of coordination or planning algorithm. This paper exploits this view to provide two coordination algorithms for distributed multi-agent systems. First we show how to achieve formation control for a class of nonholonomic mobile agents though an iterative update of each agent's angular velocity along the trajectory. The algorithm required to achieve this result uses local measurements, but a centralized computation of the control input. Second, we show a decentralized coordination strategy for a set of simple first-order integrator dynamic systems. In this case the control updates are computed locally by each agent using only local information, yet through the iterative update process the group achieves the desired formation. Numerical simulations illustrate the results.

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“…In [30] and [31], ILC approaches are utilized to improve multi-axis coordination of motion control and autonomous robotic agents, and the system model is required in controller design. In [32], a multi-agent formulation ILC is developed for nonlinear systems, and its update law does not depend on the system model.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

Sun

Hou

2013

IEEE Trans. Automat. Sci. Eng.

111

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This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.Note to Practitioners-This work was motivated by the outstanding repetitive operation pattern and the high safety requirements of high-speed railways. By utilizing the repetitive operation feature of a train, iterative learning control strategy is applied to improve control performance by learning repetitive information. To avoid excessive speed, which is regarded as a severe unsafe factor for the high-speed train, an overspeed penalty is added as a state constraint. To keep safe headway between adjacent trains, the adjacent trains' information is used to construct the coordinated iterative learning control strategy. To further enhance the applicability, the corresponding control scheme under traction/braking force constraint is also presented. In addition, this approach is a data driven model-free control method. The above method can also be applied to other repetitive operation systems with state constraint, input constraint, or requirement of coordination operation. Although the feasibility and effectiveness of this ILC-based train control method has been proved through the rigorous theoretical analyses without requirement of the train dynamics model as long as some mild reasonable assumptions are satisfied, and the simulation research as well, the field experiments have not been carried out yet. In our future research, we will focus on this issue and address some other practical concerns, such as the operation-depended uncertainties and disturbances in train operations.

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Precision coordination and motion control of multiple systems via iterative learning control

Cited by 9 publications

References 87 publications

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Multi-agent coordination by iterative learning control: Centralized and decentralized strategies

Coordinated Iterative Learning Control Schemes for Train Trajectory Tracking With Overspeed Protection

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