Jcd Jurgen van Zundert

Optimality and flexibility in Iterative Learning Control for varying tasks

Iterative Learning Control (ILC) can significantly enhance the performance of systems that perform repeating tasks. However, small variations in the performed task may lead to a large performance deterioration. The aim of this paper is to develop a novel ILC approach, by exploiting rational basis functions, that enables performance enhancement through iterative learning while providing flexibility with respect to task variations. The proposed approach involves an iterative optimization procedure after each task, that exploits recent developments in instrumental variable-based system identification. Enhanced performance compared to pre-existing results is proven theoretically and illustrated through simulation examples.

Enhancing Flatbed Printer Accuracy and Throughput: Optimal Rational Feedforward Controller Tuning Via Iterative Learning Control

Advanced control methods potentially enable performance improvements in printing systems for minor additional costs. The aim of this paper is to develop a control framework that is capable of delivering throughput and accuracy enhancements for an industrial flatbed inkjet printer. The proposed method involves iterative learning control with a rational feedforward parameterization to enable varying position references which are required for printing. Experimental results highlight the efficacy of the proposed method in a comparison with related pre-existing learning control approaches.

On the potential of lifted domain feedforward controllers with a periodic sampling sequence

Motion control applications traditionally operate with a single-rate, equidistant sampling scheme. For cost reasons, a current trend in industry is consolidating multiple applications on a single embedded platform. Generally, to deal with inter-application interference, a predictable scheduling policy allocates resource to the applications in these platforms. Realizing an equidistant sampling scheme on such shared platform is inflexible and often turns out to be expensive in terms of resource or conservative in terms of performance. The aim of this paper is to investigate the possibilities to relax the equidistant sampling convention. To this end, recent results show that platform timing properties can be represented by a known, precise, and periodically varying set of sampling periods. In view of such predictable platforms, a framework is presented for analysis and synthesis of lifted domain feedforward controllers for periodically time-varying closed-loop systems. Through simulations the potential of such periodically time-varying sampling over conservative equidistant sampling schemes is demonstrated.

Iterative Learning Control for varying tasks: Achieving optimality for rational basis functions

Iterative Learning Control (ILC) can achieve superior tracking performance for systems that perform repeating tasks. However, the performance of standard ILC deteriorates dramatically when the task is varied. In this paper ILC is extended with rational basis functions to obtain excellent extrapolation properties. A new approach for rational basis functions is proposed where the iterative solution algorithm is of the form used in instrumental variable system identification algorithms. The optimal solution is expressed in terms of learning filters similar as in standard ILC. The proposed approach is shown to be superior over existing approaches in terms of performance by a simulation example.

An approach to stable inversion of LPTV systems with application to a position-dependent motion system

Model inversion is essential in many control approaches, including inverse model feedforward and iterative learning control (ILC). The aim of this paper is the development of inversion techniques for linear periodically time-varying (LPTV) systems, possibly multivariable. The proposed method involves stable inversion, where bounded solutions are computed through a two-point boundary value problem. A key aspect herein involves a new dichotomic split for the stable and unstable dynamics, which is nontrivial for general LTV systems. As a special case, well-known stable inversion techniques for LTI systems are recovered. The approach is successfully demonstrated on a position-dependent wafer stage system.

Stable inversion of LPTV systems with application in position-dependent and non-equidistantly sampled systems

ABSTRACT Many control applications, including feedforward and learning control, involve the inverse of a dynamical system. For nonminimum-phase systems, the response of the inverse system is unbounded. For linear time-invariant (LTI), nonminimum-phase systems, a bounded, noncausal inverse response can be obtained through an exponential dichotomy. For generic linear time-varying (LTV) systems, such a dichotomy does not exist in general. The aim of this paper is to develop an inversion approach for an important class of LTV systems, namely linear periodically time-varying (LPTV) systems, which occur in, e.g. position-dependent systems with periodic tasks and non-equidistantly sampled systems. The proposed methodology exploits the periodicity to determine a bounded inverse for general LPTV systems. Conditions for existence are provided. The method is successfully demonstrated in several application cases, including position-dependent and non-equidistantly sampled systems.