Jj Joost Bolder

Rational Basis Functions in Iterative Learning Control—With Experimental Verification on a Motion System

Iterative learning control (ILC) approaches often exhibit poor extrapolation properties with respect to exogenous signals, such as setpoint variations. This brief introduces rational basis functions in ILC. Such rational basis functions have the potential to both increase performance and enhance the extrapolation properties. The key difficulty that is associated with these rational basis functions lies in a significantly more complex optimization problem when compared with using preexisting polynomial basis functions. In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems. An experimental case study confirms the advantages of rational basis functions compared with preexisting results, as well as the effectiveness of the proposed iterative algorithm.

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 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.

Inferential Iterative Learning Control:A 2D-system approach

Certain control applications require that performance variables are explicitly distinguished from measured variables. The performance variables are not available for real-time 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 first to show that the pre-existing ILC controllers may not be directly implementable in this setting, and second to develop a new approach that enables the use of different variables for feedback and batch-to-batch control. The analysis reveals that by using pre-existing ILC methods, the ILC and feedback controllers may not be stable in an inferential setting. Therefore, the complete closed-loop system is cast in a 2D framework to analyze stability. Several solution strategies are outlined. The analysis is illustrated through an application example in a printing system. Finally, the developed theory also leads to new results for traditional ILC algorithms in the common situation where the feedback controller contains a pure integrator.

On inferential Iterative Learning Control: With example to 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.

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.

Exploiting rational basis functions in iterative learning control

Iterative learning control approaches often suffer from poor extrapolability with respect to exogenous signals, including setpoint variations. The aim of this paper is to introduce rational basis functions in ILC. Such rational basis function have the potential to both increase performance and enhance extrapolability. The key caveat that is associated with these rational basis function lies in a significantly more complex optimization problem when compared to using polynomial basis functions. In this paper, a novel iterative optimization procedure is proposed that enables the use of rational basis functions in ILC. A simulation example confirms (1) the advantages of rational basis functions compared to pre-existing results, and (2) the efficacy of the proposed iterative algorithm.

Robust sawtooth period control based on adaptive online optimization

The systematic design of a robust adaptive control strategy for the sawtooth period using electron cyclotron current drive (ECCD) is presented. Recent developments in extremum seeking control (ESC) are employed to derive an optimized controller structure and offer practical tuning guidelines for its parameters. In this technique a cost function in terms of the desired sawtooth period is optimized online by changing the ECCD deposition location based on online estimations of the gradient of the cost function. The controller design does not require a detailed model of the sawtooth instability. Therefore, the proposed ESC is widely applicable to any sawtoothing plasma or plasma simulation and is inherently robust against uncertainties or plasma variations. Moreover, it can handle a broad class of disturbances. This is demonstrated by time-domain simulations, which show successful tracking of time-varying sawtooth period references throughout the whole operating space, even in the presence of variations in plasma parameters, disturbances and slow launcher mirror dynamics. Due to its simplicity and robustness the proposed ESC is a valuable sawtooth control candidate for any experimental tokamak plasma, and may even be applicable to other fusion-related control problems.

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.

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.