Showing papers by "OH Okko Bosgra published in 2008"

Fixed Structure Feedforward Controller Design Exploiting Iterative Trials: Application to a Wafer Stage and a Desktop Printer

Abstract: In this paper, the feedforward controller design problem for high-precision electromechanical servo systems that execute finite time tasks is addressed. The presented procedure combines the selection of the fixed structure of the feedforward controller and the optimization of the controller parameters by iterative trials. A linear parametrization of the feedforward controller in a two-degree-of-freedom control architecture is chosen, which results in a feedforward controller that is applicable to a class of motion profiles as well as in a convex optimization problem, with the objective function being a quadratic function of the tracking error. Optimization by iterative trials avoids the need for detailed knowledge of the plant, achieves the controller parameter values that are optimal with respect to the actual plant, and allows for the adaptation to possible variations that occur in the plant dynamics. Experimental results on a high-precision wafer stage and a desktop printer illustrate the procedure.

Residual vibration suppression using Hankel iterative learning control

Abstract: In this paper, we present a new approach for suppression of residual vibrations in point-to-point motions based on lifted iterative learning control (ILC). The approach is to add a signal to the command input during the point-to-point motion in order to compensate for residual vibrations. A special form of ILC with separate actuation and observation time windows is shown to converge to the required signal. Subsequently, we present ILC control strategies for residual vibration suppression in which convergence and performance specifications can be designed separately. Additionally, the designed controllers have the capability to constrain the amplitude of the command signal. The presented strategies are demonstrated on a flexible system and shown to be successful in the suppression of residual vibration while minimizing the maximum amplitude of the command signal. Copyright © 2007 John Wiley & Sons, Ltd.

Robustness against model uncertainties of norm optimal iterative learning control

Abstract: In this paper, we study MIMO Iterative Learning Control (ILC) and its robustness against model uncertainty. Although it is argued that, so-called, norm optimal ILC controllers have some inherent robustness, not many results are available that can make quantitative statements about the allowable model uncertainty. In this paper, we derive sufficient conditions for robust convergence of the ILC algorithm in presence of an uncertain system with an additive uncertainty bound. These conditions are applied to norm optimal ILC, resulting in guidelines for robust controller design. Theoretical results are illustrated by simulations.

Norm optimal Cross-Coupled Iterative Learning Control

Abstract: In this paper, we focus on improving contour tracking in precision motion control (PMC) applications through the use of Cross-Coupled Iterative Learning Control (CCILC). Initially, the relationship between individual axis errors and contour error is discussed, including insights into the different reasons for implementing CCILC versus individual axis ILC. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. framework is reformatted to include the contour error, as well as individual axis errors. General guidelines for tuning the different weighting matrices are presented. The weighting approach of this framework enables one to focus on individual axis or contour tracking independently. The performance benefits of N.O. CCILC versus ILC are illustrated through simulation and experimental testing on a multi-axis robotic testbed.

Robust-control-relevant coprime factor identification: A numerically reliable frequency domain approach

Abstract: In approximate identification, the goal of the model should be taken into account when evaluating model quality. The purpose of this paper is the development of a system identification procedure, resulting in model sets that are suitable for subsequent robust control design. Incorporation of control relevance in the procedure results in a closed-loop frequency response-based multivariable system identification procedure. The model is represented as a coprime factorization, enabling the usage of stable model perturbations. The main result is the direct estimation of control-relevant coprime factors, exploiting knowledge of a stabilizing controller during the identification experiment. A numerically reliable iterative algorithm is devised, which is illustrated by means of experimental results.

Estimating disturbances and model uncertainty in model validation for robust control

Abstract: Deterministic approaches to model validation for robust control are investigated. In common deterministic model validation approaches, a trade-off between disturbances and model uncertainty is present, resulting in an ill-posed problem. In this paper, an approach to model validation is presented that attempts to remedy the ill-posedness. By employing accurate, non-parametric, deterministic disturbance models in conjunction with enforcing averaging properties of deterministic disturbances, a novel framework enabling model validation for robust control is obtained. The approach results in a realistically estimated model uncertainty and a disturbance model, and is illustrated in a simulation example.

Suppressing intersample behavior in Iterative Learning Control

Abstract: Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in a realistic simulation example.

A design approach for noncausal robust Iterative Learning Control using worst case disturbance optimisation

Abstract: In this paper, we present a novel iterative learning control (ILC) strategy that is robust against model uncertainty, as given by a system model and an additive uncertainty bound. The design methodology hinges on Hinfin optimisation, however, the procedure is modified such that the ILC controller is noncausal and inherently acts on a finite time interval. The resulting controller has the structure of a norm optimal ILC controller, so that robustness can be easily assessed. Furthermore, in an example, we show that the presented robust ILC controller can outperform linear quadratic ILC controllers.

Modeling local/periodic temperature variation in catalytic reactions

Abstract: This paper introduces a dynamic simulation model for investigation of local and transient effects in catalytic reaction systems. More specifically, the model allows inspection of the effects of supplying thermal energy directly to the catalytic particles in stead of applying energy to the whole reactor volume. With simulation results it is shown that for a certain type of catalytic reaction system the obtainable yield of product and the selectivity of the reaction toward that product can be significantly increased when thermal energy is supplied directly to the catalyst. It is also shown that the results can be improved even further by supplying thermal energy periodically in stead of continuously.

Bias Removal in Higher Order Sinusoidal Input Describing Functions

Abstract: In this paper a novel method is presented for the reduction of bias caused by harmonic excitation in the identification of higher order sinusoidal input describing functions (HOSIDF). HOSIDF are a recently introduced generalization of the theory of the describing function. HOSIDF describe the magnitude and phase relations between the individual harmonic components in the output signal of a non-linear system and the sinusoidal excitation signal. In the presented method, the output signal of a non-linear system subjected to harmonic excitation is numerically split up into a fraction caused by the non-linear response due to the fundamental input signal component and the fraction caused by the quasi-linear response due to the harmonic input signal components. This separation is based on the assumption that the non-linear effects of intermodulation can be neglected, compared to the the effects caused by the generation of harmonics and gain compression/expansion. The method is demonstrated with real measurements on a mechanical system with friction.