101 research outputs found
Accurate H-infinity-norm estimation via finite-frequency norms of MIMO local parametric models
Efficient MIMO Iterative Feedback Tuning via Randomization
Iterative feedback tuning (IFT) enables the tuning of feedback controllers based on measured data without the need for a parametric model. The aim of this paper is to develop an efficient method for MIMO IFT that reduces the required number of experiments. Using a randomization technique, an unbiased gradient estimate is obtained from a single dedicated experiment, regardless of the size of the MIMO system. This gradient estimate is employed in a stochastic gradient descent algorithm. Simulation examples illustrate that the approach reduces the number of experiments required to converge
Accurate H-infinity-norm estimation via finite-frequency norms of MIMO local parametric models
Intermittent sampling in iterative learning control:A monotonically-convergent gradient-descent approach with application to time stamping
Intermittent sampling in iterative learning control:A monotonically-convergent gradient-descent approach with application to time stamping
Gaussian Process Repetitive Control for Suppressing Spatial Disturbances:With Application to a Substrate Carrier System
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