Data-driven optimal ILC for multivariable systems : removing the need for L and Q filter design

Many iterative learning control algorithms rely on a model of the system. Although only approximate model knowledge is required, the model quality determines the convergence and performance properties of the learning control algorithm. The aim of this paper is to remove the need for a model for a class of multivariable ILC algorithms. The main idea is to replace the model by dedicated experiments on the system. Convergence criteria are developed and the results are illustrated with a simulation on a multi-axis flatbed printer

Intelligente regeltechniek voor nieuwe generatie mechatronica

Intelligentere aansturing is essentieel om toekomstige mechatronische systemen aan de steeds strenger wordende nauwkeurigheids- en snelheidseisen te laten voldoen. Recente doorbraken op regeltechnisch gebied door intensieve samenwerking tussen academia en research- en developmentafdelingen maken het mogelijk om aan deze toekomstige producteisen te voldoen. De recente installatie van een hightech flatbed printer van Océ bij de Control Systems Technology groep en de oprichting van het High Tech Systems Center moeten de ontwikkeling van een systematische en industrieel toepasbare aanpak voor intelligente aansturing voor mechatronische systemen mogelijk maken

Aspects in inferential iterative learning control : a 2D systems analysis

Increasing performance requirements lead to a situation where performance variables need to be explicitly distinguished from measured variables. The performance variables are not available for feedback. Instead, they are often available after a task. This enables the application of batch-to-batch control strategies such as Iterative Learning Control (ILC) to the performance variables. The aim of this paper is to reveal potential problems in combining ILC and feedback control for this scenario, and to propose a solution. The time-trial dynamics of a common ILC algorithm with dynamic learning filters are cast into discrete linear repetitive processes, a class of 2D systems. Appropriate 2D stability notions are connected to well-known conditions on the ILC algorithm. The analysis reveals that there are important cases where the ILC and feedback combination is not stable in a 2D sense. A solution to deal with such cases is proposed. The analysis is supported with a simulation example of medium positioning drive in a printing system

On inferential iterative learning control : with example on a printing system

Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example

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

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

Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: enabling large feedforward tasks on a position-dependent printer

Iterative learning control (ILC) enables high performance for systems that execute repeating tasks. Norm-optimal ILC based on lifted system representations provides an analytic expression for the optimal feedforward signal. However, for large tasks the computational load increases rapidly for increasing task lengths, compared to the low computational load associated with so-called frequency domain ILC designs. The aim of this paper is to solve norm-optimal ILC through a Riccati-based approach for a general performance criterion. The approach leads to exactly the same solution as found through lifted ILC, but at a much smaller computational load (O(N) vs O(N^3)) for both linear time-invariant (LTI) and linear time-varying (LTV) systems. Interestingly, the approach involves solving a two-point boundary value problem (TPBVP). This is shown to have close connections to stable inversion techniques, which are central in typical frequency domain ILC designs. The proposed approach is implemented on an industrial flatbed printer with large tasks which cannot be implemented using traditional lifted ILC solutions. The proposed methodologies and results are applicable to both ILC and rational feedforward techniques by applying them to suitable closed-loop or open-loop system representations. In addition, they are applied to a position-dependent system, revealing necessity of addressing position-dependent dynamics and confirming the potential of LTV approaches in this situation

Iterative learning control with basis functions for media positioning in scanning inkjet printers

In printing systems, the positioning accuracy of the medium with respect to the print heads directly impacts print quality. In a regular document inkjet printer, the main task of the media positioning drive is to shift the medium after the printhead has finished a pass. Most media have the tendency to deform while it is being printed due to variations in temperature and moisture content. In order to improve print quality, we propose to move the medium during printing to counteract the deformation. These small scale trajectories are performed in an operating regime of which the dynamics considerably differ from the regular transportation step. Using iterative learning control with basis functions for both positioning tasks, the positioning accuracy of the drive is improved substantially; while keeping numerical cost low

Robust adaptive control of the sawtooth instability in nuclear fusion

The sawtooth instability is a repetitive phenomenon occurring in plasmas of tokamak nuclear fusion reactors. Experimental studies of these instabilities and the effect they have on the plasma (notably the drive of secondary instabilities and consequent performance reduction) for a wide variety of plasma conditions is an important line of study in nuclear fusion research. Variations in the plasma conditions have a significant influence on the dynamical behavior of the sawtooth instability. Therefore, this paper presents the design of a sawtooth period controller which is robust against such variations. The controller is from a class of adaptive controllers better known as extremum seekers. In this technique, a cost function in terms of the desired sawtooth period is optimized on-line. The Extremum Seeking Controller (ESC) is model-free and is therefore inherently robust against model uncertainty. Simulations show that the controller is robust against variations in plasma parameters, delay in the sawtooth crash detection, and noise on the in- and outputs of the process. Because of its robustness, ESC is a promising candidate strategy for a wide range of fusion-related control problems with high model uncertainty