Estimation-based ILC using particle filter with application to industrial manipulators

Axelsson, Patrik; Karlsson, Rickard; Norrlöf, Mikael

doi:10.1109/iros.2013.6696584

Cited by 2 publications

(1 citation statement)

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“…Estimation-based ilc using more advanced Bayesian techniques than ekf such as pf and unscented Kalman filter have been used in [8] together with filterbased ilc. This paper generalizes the ideas in [8,[25][26][27] to the case where the full probability density function (pdf) of the estimated quantity is used in an optimisation problem similar to the norm-optimal ilc [2,3]. In addition, the full knowledge of the estimated state vector from previous ilc iteration, enables non-linear extensions by utilising linearisation solutions in the proposed estimation-based ilc method.…”

Section: Introductionmentioning

confidence: 99%

Estimation-based norm-optimal iterative learning control

Axelsson¹,

Karlsson²,

Norrlöf³

2014

Systems & Control Letters

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The iterative learning control (ilc) method improves performance of systems that repeat the same task several times. In this paper the standard norm-optimal ilc control law for linear systems is extended to an estimation-based ilc algorithm where the controlled variables are not directly available as measurements. The proposed ilc algorithm is proven to be stable and gives monotonic convergence of the error. The estimationbased part of the algorithm uses Bayesian estimation techniques such as the Kalman lter. The objective function in the optimisation problem is modied to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. It is further shown that for linear time-invariant systems the ilc design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and veried in a simulation study with a simplied model of an industrial manipulator system. AbstractThe iterative learning control (ilc) method improves performance of systems that repeat the same task several times. In this paper the standard norm-optimal ilc control law for linear systems is extended to an estimation-based ilc algorithm where the controlled variables are not directly available as measurements. The proposed ilc algorithm is proven to be stable and gives monotonic convergence of the error. The estimation-based part of the algorithm uses Bayesian estimation techniques such as the Kalman filter. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. It is further shown that for linear time-invariant systems the ilc design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and verified in a simulation study with a simplified model of an industrial manipulator system.

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