Iterative learning control and repetitive control for engineering practice

Longman, Richard W.

doi:10.1080/002071700405905

Cited by 699 publications

(464 citation statements)

References 22 publications

(18 reference statements)

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“…As the learning transient (transient growth) is the major problem for the applicability of a nonlinear ILC [15], we use the bounded error ILC algorithm [10] in order to solve this problem in a safe, fast and simple manner and consequently multirate ILC schemes are not needed to solve the learning transient problem.…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Iterative learning control with sampled-data feedback for robot manipulators

Delchev¹,

Boiadjiev²,

Kawasaki³

et al. 2014

Archives of Control Sciences

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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.

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Section: Formulation Of the Problemmentioning

confidence: 99%

Iterative learning control with sampled-data feedback for robot manipulators

Delchev¹,

Boiadjiev²,

Kawasaki³

et al. 2014

Archives of Control Sciences

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“…The benefit of RC is that it does not require the same initial condition each trial. For more detail on discrete RC see [7], [16], [17].…”

Section: Office Building Temperature Controlmentioning

confidence: 99%

“…have cutoff frequency above L in the low-pass case and is there to ensure that the condition for stability is met. Stability can be checked using (9) (derivation in [7]), which must be satisfied for all frequencies.…”

Section: Office Building Temperature Controlmentioning

confidence: 99%

“…This paper investigates the use of learning type control such as iterative learning control (ILC) and repetitive control (RC) for improvement of existing feedback controllers in buildings such as the ones mentioned above. A survey on ILC is provided in [6] and RC is covered in references such as [7], [8], which also show the similarity of these methods. The basic concept of these types of learning control methodologies is that feedforward signals are generated and updated based on previous errors and they do not require the costly process of identifying system models.…”

Section: Introductionmentioning

confidence: 99%

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Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

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Abstract-In this paper, learning/repetitive control is proposed for improvement of existing feedback control loops for temperature regulation in buildings. A single zone office building is used as an example, with real weather data for Phoenix Arizona and realistic occupancy load schedules. Simulations have shown a decrease in the average set point tracking error of more than 50%, even without additional energy consumption. This can be achieved in situations where the load disturbances have enough repeatability and a repeatable-to-nonrepeatable ratio can be computed to determine if learning should be used and at which frequencies. Furthermore, the increased tightness in reference tracking could be used to lower energy consumption by moving the reference set point closer to the boundaries of the allowable temperature range.

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“…The robustness of a variety of ILC approaches has been discussed in literature: inverse model-based ILC [3], linear ILC [4], norm-optimal ILC [5], two dimensional learning system [6], and gradient-based ILC algorithms [7]. In general, these papers derive ILC convergence conditions.…”

Section: Introductionmentioning

confidence: 99%

Robust optimal iterative learning control with model uncertainty

Son

Pipeleers

Swevers

2013

52nd IEEE Conference on Decision and Control

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Abstract-In this paper we present an approach to deal with model uncertainty in norm-optimal iterative learning control (ILC). Model uncertainty generally degrades the convergence and performance of conventional learning algorithms. To deal with model uncertainty, a robust worst-case norm-optimal ILC is introduced. The problem is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. The paper also investigates the relationship between the proposed approach and conventional norm-optimal ILC; where it is found that our design method is equivalent to conventional norm-optimal ILC with trial-varying learning gains. Finally, simulation results of the presented technique are given.

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Iterative learning control and repetitive control for engineering practice

Cited by 699 publications

References 22 publications

Iterative learning control with sampled-data feedback for robot manipulators

Iterative learning control with sampled-data feedback for robot manipulators

Learning/repetitive control for building systems with nearly periodic disturbances

Robust optimal iterative learning control with model uncertainty

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