Design strategy for iterative learning control based on optimal control

Tousain, R.L.; Meche, E. van der; Bosgra, О.H.

doi:10.1109/cdc.2001.980905

Cited by 46 publications

(23 citation statements)

References 5 publications

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“…Based on a so-called 'lifted' domain representation, cf. Phan and Longman (1988), Moore (1998), Amann et al (1996), LQG-type solutions have been proposed by Lee et al (2001), Cho et al (2005), Tousain et al (2001), Rice and Verhaegen (2010), Ahn et al (2007) for estimating the tracking error and minimizing a quadratic cost function. Work in Bristow et al (2006), Chin et al (2004), Cho et al (2005), Barton et al (2011) has shown that ILC can be applied to systems with underlying feedback loops.…”

Section: Related Workmentioning

confidence: 99%

Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

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Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance-even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-theart optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arElectronic supplementary material The online version of this article (bitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.

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Section: Related Workmentioning

confidence: 99%

Optimization-based iterative learning for precise quadrocopter trajectory tracking

2012

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“…This comes down to trading off nominal performance, represented by the asymptotic tracking error, for robustness to system uncertainty. The weighting matrix W δu regulates the effect of noise and trial-varying disturbances on the next iteration's input signal and influences therefore also the asymptotic error [15]. By tuning W δu , convergence speed is traded off for the effect of noise and trial-varying disturbances on the next iteration's input signal.…”

Section: A Norm-optimal Ilcmentioning

confidence: 99%

A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI Systems

Janssens

Pipeleers

Swevers

2013

IEEE Trans. Contr. Syst. Technol.

157

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Abstract-This paper presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-topoint motion problems. The key contribution of this paper is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating timeconsuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to (i) achieve tracking accuracy up to the repeatability of the test setup, (ii) minimize the rms value of the tracking error while respecting the actuator input constraints, (iii) learn energyoptimal system inputs for point-to-point motions.

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“…For the analysis in Section V, the matrix description of the system and the ILC algorithm is used, which is closely related to the descriptions of systems using ILC in [12], [13] and [14] among others. First, define the vector r of the Nsample sequence of the reference signal r as…”

Section: System Description In Matrix Formmentioning

confidence: 99%

A framework for analysis of observer‐based ILC

Wallén

Norrlöf

Gunnarsson

2011

Asian Journal of Control

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A framework for Iterative Learning Control (ILC) is proposed for the situation when the ILC algorithm is based on an estimate of the controlled variable obtained from an observer-based estimation procedure. Under the assumption that the ILC input converges to a bounded signal, a general expression for the asymptotic error of the controlled variable is given. The asymptotic error is then exemplified by an ILC algorithm applied to a flexible two-mass model of a robot joint.Keywords: Iterative learning control; Framework, Estimate, Asymptotic, Controlled variable A framework for analysis of observer-based ILC Johanna Wallén, Mikael Norrlöf and Svante GunnarssonAbstract-A framework for Iterative Learning Control (ILC) is proposed for the situation when the ILC algorithm is based on an estimate of the controlled variable obtained from an observer-based estimation procedure. Under the assumption that the ILC input converges to a bounded signal, a general expression for the asymptotic error of the controlled variable is given. The asymptotic error is then exemplified by an ILC algorithm applied to a flexible two-mass model of a robot joint.

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Design strategy for iterative learning control based on optimal control

Cited by 46 publications

References 5 publications

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Optimization-based iterative learning for precise quadrocopter trajectory tracking

A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI Systems

A framework for analysis of observer‐based ILC

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