Robust iterative learning control with current feedback for uncertain linear systems

Doh, Tae-Yong

doi:10.1080/002077299292650

Cited by 48 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More generally the gap or co-prime factor uncertainty model adopted is more general than the other previously considered results in ILC which are restricted to uncertainty models of a multiplicative type, e.g. [10], [14]. In contrast to the situation for LTI plants and controllers, the robustness margins are dependent on the disturbance level: this appears to be a feature of 'learning' systems, see for example the recent related results in adaptive control [4,5] where the margin also is of this form.…”

Section: Discussionmentioning

confidence: 99%

“…[15]). In particular, [8], asserts robustness to positive real multiplicative uncertainties (limited by ±90 • phase variations over all frequencies) and [14] and [10] also consider multiplicative perturbations, and relate robust stability of an underlying feedback controller to the related ILC algorithm. These earlier results are of great interest, but the conditions are too strong for practical use.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust stability of iterative learning control schemes

French

2007

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

A notion of robust stability is developed for iterative learning control in the context of disturbance attenuation. The size of the unmodelled dynamics is captured via a gap distance, which in turn is related to the standard H 2 gap metric, and the resulting robustness certificate is qualitatively equivalent to that obtained in classical robust H ∞ theory. A bound on the robust stability margin for a specific adaptive ILC design is established.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust stability of iterative learning control schemes

French

2007

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…To solve this problem, a two-step minimization procedure is presented in which the learning function is designed first and the feedback controller second. In [35], [54], an indirect linear fractional transformation is used to place the current-iteration ILC in standard form for H ∞ synthesis. This approach allows for the simultaneous design of the feedback controller and learning function.…”

Section: H ∞ Methodsmentioning

confidence: 99%

A survey of iterative learning control

Bristow

Tharayil²,

Alleyne³

2006

IEEE Control Syst.

2,423

View full text Add to dashboard Cite

I terative learning control (ILC) is based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions (trials, iterations, passes). For instance, a basketball player shooting a free throw from a fixed position can improve his or her ability to score by practicing the shot repeatedly. During each shot, the basketball player observes the trajectory of the ball and consciously plans an alteration in the shooting motion for the next attempt. As the player continues to practice, the correct motion is learned and becomes ingrained into the muscle memory so that the shooting accuracy is iteratively improved. The converged muscle motion profile is an open-loop control generated through repetition and learning. This type of learned open-loop control strategy is the essence of ILC.We consider learning controllers for systems that perform the same operation repeatedly and under the same operating conditions. For such systems, a nonlearning con-troller yields the same tracking error on each pass. Although error signals from previous iterations are information rich, they are unused by a nonlearning controller. The objective of ILC is to improve performance by incorporating error information into the control for subsequent iterations. In doing so, high performance can be achieved with low transient tracking error despite large model uncertainty and repeating disturbances. ILC differs from other learning-type control strategies, such as adaptive control, neural networks, and repetitive control (RC). Adaptive control strategies modify the controller, which is a system, whereas ILC modifies the control input, which is a signal [1]. Additionally, adaptive controllers typically do not take advantage of the information contained in repetitive command signals. Similarly, neural network learning involves the modification of controller parameters rather than a control signal; in this case, large networks of nonlinear neurons are modified. These large networks require extensive training data, and fast convergence may be difficult to

show abstract

“…Because ILC is an open-loop control scheme, when an inappropriate initial control law is chosen, this control schemes may generate harmful effects [7]. To overcome this drawback, it is usually applied to real systems along with feedback control for enhancing robustness against unrepeatable disturbances and for reducing the tracking error in the early stage of learning [8]. It is desirable that the least information about the plant is required in design of an iterative learning controller.…”

Section: Iterative Learning Controlmentioning

confidence: 99%

Internal Model Based Iterative Learning Control for Linear Motor Motion Systems

Fan

Wang

2006

Sixth International Conference on Intelligent Systems Design and Applications

View full text Add to dashboard Cite

Nonlinear disturbances are the main reasons why the linear motor motion systems can not reach high tracking accuracy. Combining iterative learning control (ILC) with internal model control (IMC), an internal model based iterative learning controller is proposed for linear motor motion systems, which is inherently repetitive in terms of motion trajectories. The ILC is used to increase the tracking accuracy. The IMC part guarantees robust performance. Simulation results show that the IMC based iterative learning control scheme can guarantee robust performance and high tracking accuracy simultaneously.

show abstract

Robust iterative learning control with current feedback for uncertain linear systems

Cited by 48 publications

References 0 publications

Robust stability of iterative learning control schemes

Robust stability of iterative learning control schemes

A survey of iterative learning control

Internal Model Based Iterative Learning Control for Linear Motor Motion Systems

Contact Info

Product

Resources

About