Lennart Blanken

Optimal estimation of rational feedforward controllers: An instrumental variable approach

Iterative control enables a significant control performance enhancement by learning feedforward command signals from previous tasks in a batch-to-batch fashion. The aim of this paper is to develop an approach to estimate the parameters of rational feedforward controllers that provide high performance and extrapolation capabilities towards varying tasks. An instrumental variable-based algorithm is developed that leads to unbiased parameter estimates and optimal accuracy in terms of variance. The approach also enables optimal estimation of a feedforward controller using a polynomial basis. Simulation results confirm that optimal accuracy is obtained with the proposed approach.

Batch-to-Batch Rational Feedforward Control: From Iterative Learning to Identification Approaches, With Application to a Wafer Stage

Feedforward control enables high performance for industrial motion systems that perform nonrepeating motion tasks. Recently, learning techniques have been proposed that improve both performance and flexibility to nonrepeating tasks in a batch-to-batch fashion by using a rational parameterization in feedforward control. This paper aims to unify these approaches through a single framework that provides transparent connections and clear differences between the alternatives. Experimental results on an industrial motion system confirm the theoretical findings and illustrate benefits of rational feedforward tuning in motion systems, including preactuation and postactuation.

Rational iterative feedforward tuning: Approaches, stable inversion, and experimental comparison

Feedforward control plays a key role in achieving high performance for industrial motion systems that perform non-repeating motion tasks. Recently, learning techniques have been proposed to further improve both performance and robustness to non-repeating tasks by using a rational feedforward basis. The aim of this paper is to propose a unifying framework which connects these approaches. Experimental results on an industrial motion system validate the approaches and illustrate benefits of rational feedforward tuning in motion systems, including pre- and post-actuation through stable inversion.

Design and modeling aspects in multivariable iterative learning control

Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation study.