Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System

Abstract

Abstract-Index Terms-data-driven control, high-precision wafer stages, iterative feedback tuning (IFT), robust control.

Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

“…One shortcoming of these methods if applying them to our system is that the dynamic modelling of pneumatic muscles and robot behaviour would bring heavy computation burden to the control system, and it is also difficult to model the patient's arbitrary activities. One advantage of IFT technique is its ability to learn from repeating scenarios and optimise the controller parameters without knowledge of the actual system, which guarantees the inherent robustness of such a method [15,17,28]. To obtain a more robust control system, the design criterion or objective function of IFT must be taken into account.…”

“…To obtain a more robust control system, the design criterion or objective function of IFT must be taken into account. Introduce a plenty function to the input signal for certain restrictions is a typical approach to increase the system robustness, such as the studies in [17] and [28]. However, in rehabilitation scenarios the robot system will have various desired and actual output, error, as well as control input signal, especially when treat patients in different recovery stages and with different active intentions.…”

Complete dynamic model of the Twin Rotor MIMO System (TRMS) with experimental validation

“…In current work, we are investigating on the implementation of the robust control law developed in [1,2,16], and in particular on the inclusion of an anti-windup scheme to compensate for input saturations [17,18]. In addition, we plan to adopt the TRMS as benchmark to develop the use of data-based control tuning methods [19,20,21].…”

Iterative feedback tuning of wind turbine controllers

“…This is caused by the fact that, typically, there are no guarantees that the controller remains stable during the iteration. For that reason a number of articles (Heertjes et al, 2016;Procházka et al, 2005;de Bruyne and Kammer, 1999;Veres and Hjalmarsson, 2002) have appeared on the subject of including stability constraints for the optimization procedure. These algorithms are not considered here because no stability issues are encountered in this study.…”

“…The key advantage of IFT is that detailed knowledge of the (wind turbine) system is not needed, while the main requirement is that the initial closedloop system is stable, implying that it can be directly used for optimization of wind turbine controllers. IFT has been successfully applied to various application fields, including mechatronics (Al Mamun et al, 2007;Heertjes et al, 2016), robotics (Liu et al, 2011), process industry (Lequin, 1997), and recently wind turbines .…”