Iterative learning control — An optimization paradigm

Owens, D.H.

doi:10.1016/j.arcontrol.2005.01.003

Cited by 224 publications

(193 citation statements)

References 14 publications

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“…This area was motivated originally in the context of robotics [1], and has subsequently developed a more abstract footing in general control theory, and applications in a variety of domains have been considered: see for example the surveys [11], [12], and see [2], [3] for representative recent application examples. It is pertinent to observe that both iterative learning control and the related area of repetitive control has achieved considerable success in applications, perhaps in contrast to the other major theory of learning in control, namely adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Robust stability of iterative learning control schemes

French

2007

Intl J Robust & Nonlinear

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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.

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Section: Introductionmentioning

confidence: 99%

Robust stability of iterative learning control schemes

French

2007

Intl J Robust & Nonlinear

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“…The evolution process of such an algorithm has already been systematically elaborated in the reference part: an optimal iterative learning control algorithm advanced by Amann et al in terms of the time-invariant linear discrete system, which can realize monotonic and geometrical convergence of tracking error [2]; Owens, Fang and Hätönen et al have put forward a parameter optimal iterative learning control (POILC) algorithm [3]; afterwards, Hätönen and Owens raised the high-order parameter optimal iterative learning control algorithm [4,5]. Although these algorithms are structurally simple and can realize monotonic and geometrical convergence of error to zero, the rate and efficiency of error convergence are still quite not satisfying.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control Based on Niche Shuffled Frog Leaping Algorithm Research

Hao¹,

Wang²

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Aiming at slow convergence and low precision optimization in iterative learning control, a niche shuffled frog leaping algorithm was proposed in this paper, which combined memes algorithm and particle swarm algorithm, using the niche shuffled frog leaping algorithm based on the restrictive competition, avoiding the paedogenesis effectively improving the convergence speed and optimization accuracy. In order to achieve less error and monotone convergence in the iterative domain, get better transient tracking performance and establish a fast PID parameter optimization iterative learning control algorithm based on the discrete norm performance index, the PID controller was introduced into the iterative learning control parameter optimization algorithm to expand the algorithm's dimension, increase the degree of freedom in the optimal parameters, and ultimately promote the learning efficiency.

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“…This iteration-based approach is essentially different to the method of Iterative Learning Control (ILC) that was elaborated for robots repetitively executing the same task (e.g. [33,34,35,36,37]). The original transformation introduced in [27] was called Robust Fixed Point Transformation (RFPT) that contained only three adaptive control parameters.…”

Section: Introductionmentioning

confidence: 99%

Contradiction Resolution in the Adaptive Control of Underactuated Mechanical Systems Evading the Framework of Optimal Controllers

Tar

Bitó

Rudas

2016

APH

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In the practice, precise and efficient control is needed for certain state variables of multiple variable physical systems in which the number of the independent control variables is less than that of the independent state variables. In such cases, either the propagation of certain state variables is completely abandoned or the concept of the Model Predictive Control (MPC) is applied in which the model of the controlled system is embedded into the mathematical framework of the Optimal Controllers. This approach uses a cost function that summarizes the contributions of the frequently contradictory requirements. By minimizing this cost a kind of "compromise" is achieved. Whenever approximate and/or incomplete system models are available, the use of this controller is justified only for short time-intervals. The only way to reduce the accumulation of the effects of the modeling errors is the frequent re-design of the time horizon from the actual state as initial state that is done by the Receding Horizon Controllers. The more sophisticated Adaptive Controllers are designed by the use of Lyapunov's "Direct Method" that has a complicated mathematical framework that cannot easily be combined with that of the optimal controllers. As a potential competitor of the Lyapunov function-based adaptive controllers a Fixed Point Transformation-based approach was invented that in the first step transforms the the problem of computing the control signal into the task of finding an appropriate fixed point of a contractive map. The fixed point can be found by iteration in which the iterative sequence is generated by this contracting map. This method can be used for contradiction resolution without the minimization of any cost function by tracking the observable state components with time-sharing on a rotary basis.In the present paper a novel fixed point transformation is introduced. It is shown that this construction for monotonic response function of bounded derivative can guarantee global stability. Furthermore, the time-sharing-based method is demonstrated by the control of an underactuated 3 DoF Classical Mechanical system via numerical simulations.

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Iterative learning control — An optimization paradigm

Cited by 224 publications

References 14 publications

Robust stability of iterative learning control schemes

Robust stability of iterative learning control schemes

Iterative Learning Control Based on Niche Shuffled Frog Leaping Algorithm Research

Contradiction Resolution in the Adaptive Control of Underactuated Mechanical Systems Evading the Framework of Optimal Controllers

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