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

Nonlinear Repetitive Control for Mitigating Noise Amplification

Repetitive control can lead to high performance by attenuating periodic disturbances completely, yet it may amplify non-periodic disturbances. The aim of this paper is to achieve both fast learning and low errors in repetitive control. To this end, a nonlinear learning filter is introduced that distinguishes between periodic and non-periodic errors based on their typical amplitude characteristics to adapt the extent to which they are included in the repetitive controller. Convergence conditions for the nonlinear repetitive control system are derived by casting the resulting closed-loop as a discrete-time convergent system. Simulation results of the proposed approach demonstrate fast learning and small errors

Long-range piezo actuators: Compensating hysteresis and commutation angle reproducible disturbances

Piezo-stepper actuators consist of piezo elements that are able to displace a mover over an infinite stroke through walking while maintaining the high accuracy and high stiffness properties of the piezo elements. In Figure 1, a schematic representation of a piezo-stepper actuator is depicted