Online identification of a robot using batch adaptive control

Bukkems, Bhm Björn; Kostic, D Dragan; Jager, AG Bram de; Steinbuch, M Maarten

doi:10.1016/s1474-6670(17)34878-4

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Before all three experiments, the initial value of the vector of parameters to be estimated, , was set to zero. Consequently, the extra input in (7) was also zero during the first trial, see (15).…”

Section: ) Parameter Estimation Resultsmentioning

confidence: 99%

“…The scalar in (12) and the weighting matrix in (15), were tuned such as to achieve fast convergence of the estimates to steadystate values. Fig.…”

Section: ) Parameter Estimation Resultsmentioning

confidence: 99%

“…The design suggested in [4] is promoted in this paper, too. A preliminary presentation of the parameter estimation using batch adaptive control can be found in [15].…”

Section: Model-and Learning-based Robot Motion Controlmentioning

confidence: 99%

“…The batch adaptive update law is given as follows [14]: (15) where denotes the number of the trial , is an positive definite matrix called the adaptation gain, and represents the inner product between and , weighted by a positive definite matrix . For two matrices of equal number of rows, this inner product is defined as…”

Section: A Parameter Estimation Via Batch Adaptive Controlmentioning

confidence: 99%

“…The convergence of the tracking error implies convergence of the elements in to steady-state values . Theoretically, there is no restriction on the initial guess of , required in (15). Practically, when control inputs are bounded, cannot be chosen arbitrarily, but should be constrained to a certain feasible region corresponding to limits on the control inputs.…”

Section: A Parameter Estimation Via Batch Adaptive Controlmentioning

confidence: 99%

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Learning-based identification and iterative learning control of direct-drive robots

Bukkems

Kostic²,

Jager

et al. 2005

IEEE Trans. Contr. Syst. Technol.

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A combination of model-based and iterative learning control (ILC) is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the performance of motion control. The model-based part of the controller compensates much of the nonlinear and coupled robot dynamics. A new procedure for estimating the parameters of the rigid body model, implemented in this part of the controller, is used. This procedure is based on a batch-adaptive control algorithm that estimates the model parameters online. Information about the dynamics not covered by the rigid body model, due to flexibilities, is acquired experimentally, by identification. The models of the flexibilities are used in the design of the iterative learning controllers for the individual joints. Use of the models facilitates quantitative prediction of performance improvement via ILC. The effectiveness of the combination of the model-based and the iterative learning controllers is demonstrated in experiments on a spatial serial direct-drive robot with revolute joints.

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Section: ) Parameter Estimation Resultsmentioning

confidence: 99%

“…The scalar in (12) and the weighting matrix in (15), were tuned such as to achieve fast convergence of the estimates to steadystate values. Fig.…”

Section: ) Parameter Estimation Resultsmentioning

confidence: 99%

“…The design suggested in [4] is promoted in this paper, too. A preliminary presentation of the parameter estimation using batch adaptive control can be found in [15].…”

Section: Model-and Learning-based Robot Motion Controlmentioning

confidence: 99%

Section: A Parameter Estimation Via Batch Adaptive Controlmentioning

confidence: 99%

Section: A Parameter Estimation Via Batch Adaptive Controlmentioning

confidence: 99%

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