19 research outputs found
Accurate H-infinity-norm estimation via finite-frequency norms of MIMO local parametric models
Accurate H-infinity-norm estimation via finite-frequency norms of MIMO local parametric models
Commutation-angle domain iterative learning control: Learning waveforms for piezo stepper actuators
Long-range piezo actuators: Compensating hysteresis and commutation angle reproducible disturbances
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
A one-step approach for centralized overactuated motion control of a prototype reticle stage
Next-generation motion stages are envisaged to be lightweight to meet stringent demands regarding accuracy and throughput. The lightweight stage design implies flexible dynamical behavior, which is foreseen to be severely excited due to increasing acceleration forces. The aim of this paper is to exploit additional actuators and sensors to explicitly control the flexible dynamic behavior. A systematic weighting filter design procedure is introduced which is tailored to next-generation motion stages. The presented procedure naturally connects to existing robust control techniques. The procedure is applied to an experimental next-generation reticle stage confirming performance enhancement by exploiting additional actuators and sensors beyond traditional performance limitations
Towards Model Order Selection for Robust-Control-Relevant System Identification
Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control