Optimization-based iterative learning control for trajectory tracking

Schöllig, Angela; D’Andrea, Raffaello

doi:10.23919/ecc.2009.7074619

Cited by 42 publications

(56 citation statements)

References 18 publications

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“…2. From the update control law (2) follows, that the learning gains and are positive constants, which should be preselected. From the workflow chart, there is another constant , which determines whether iteration must be stopped or continued during the trajectory tracking process.…”

Section: Fig 2 Bea Workflow Chartmentioning

confidence: 99%

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Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

Yovchev¹

2017

Journal of Theoretical and Applied Mechanics

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This paper continues previous research of the Bounded Error Algorithm (BEA) for Iterative Learning Control (ILC) and its application into the control of robotic manipulators. It focuses on investigation of the influence of the parameters of BEA over the convergence rate of the ILC process. This is performed first through a computer simulation. This simulation suggests optimal values for the parameters. Afterwards, the estimated results are validated on a physical robotic manipulator arm. Also, this is one of the first reports of applying BEA into robots control.

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Section: Fig 2 Bea Workflow Chartmentioning

confidence: 99%

“…ILC for robotic arms is a class of self-tuning algorithms, which repeatedly implement assigned tasks of robot motions in order to minimize trajectory-tracking errors [1,2].…”

Section: Introductionmentioning

confidence: 99%

Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

Yovchev¹

2017

Journal of Theoretical and Applied Mechanics

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“…This extension is applicable to both model-based and data-driven normoptimal ILC [12]. The application to open-loop and closedloop systems is discussed.…”

Section: Norm-optimal Ilc For Lti Systems With Actuator Constraintsmentioning

confidence: 99%

“…Traditional norm-optimal ILCs use the analytical solution of an unconstrained convex quadratic program (QP), and hence do not allow actuator constraints to be taken into account explicitly. For this reason, [12] proposes to solve a constrained convex QP at every iteration allowing actuator limitations to be formulated as linear inequality constraints. This way, the model-based norm-optimal ILC framework is extended for systems with actuator constraints.…”

Section: Introduction Iterative Learning Control (Ilc)mentioning

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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“…The study [8] also discusses these issues, based on simulations of a realistic robot model. In [9] the model error of a state-space model linearised along the desired trajectory is estimated using a Kalman filter in the iterationdomain. The control signal at next iteration is then given by minimising the deviation of the states from the desired trajectory.…”

Section: Introductionmentioning

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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Optimization-based iterative learning control for trajectory tracking

Cited by 42 publications

References 18 publications

Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

Finding the Optimal Parameters for Robotic Manipulator Applications of the Bounded Error Algorithm for Iterative Learning Control

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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