Search

Iterative learning control (ILC) enables high performance for motion systems executing repetitive tasks. The robustness filter of ILC enhances the robustness w.r.t. model uncertainties and disturbances, but results in that the repetitive error cannot be eliminated. In this article, a dual-loop ILC (DILC) approach is proposed for precision motion systems to explicitly address the design tradeoff of standard ILC between robustness and tracking performance. In the proposed DILC approach, the standard ILC is paralleled with an additional feedforward signal. When ILC converges, the additional feedforward signal is updated by the converged total feedforward signal, and then, the ILC begins a new iteration. As a result, the nonzero asymptotic error caused by the robustness filter is eliminated by adding an iterative action over the feedforward signal onto ILC. Comparative simulation and experimental results confirm that, compared to ILC, the proposed DILC can significantly enhance the tracking performance without the sacrifice of robustness w.r.t. model uncertainties and disturbances. Application to an ultraprecision wafer stage illustrates that the proposed DILC decreases the peak values of moving average and moving standard deviation of the tracking error by 52.7% and 43.9%, respectively. [ABSTRACT FROM AUTHOR]

Ball screw feed-drive system (BSFDS) is the precision transmission mechanism widely used in micron-scale positioning or motion trajectory control. Its desired specifications including high acceleration, speed, accuracy, and stability are challenged by vibration, friction, thermal error, uncertainty, etc. Inspired by these challenges, the modeling and control issues have been widely studied and discussed for decades. This paper presents an overview of modeling and control approaches, including identification, linear parameter varying, thermal error modeling and control, nonlinear control, and robust control. In particular, it reviews the emerging control issues and approaches, such as artificial intelligence, learning control, and data-driven control, which have increased in recent years. [ABSTRACT FROM AUTHOR]

In precision motion systems, well-designed feedforward control can effectively compensate for the reference-induced error. Compared to iterative learning control (ILC), data-based fixed-structure feedforward control (DFFC) possesses robustness against reference variations, but results in mediocre performance in exactly repeating tasks. In this paper, a novel data-based switching feedforward control (DSFC) approach is synthesized to well balance the tradeoff between the extrapolation capabilities and servo performance. Specifically, a theoretical framework is developed for the proposed DSFC approach, where the feedforward control is switched between ILC and DFFC according to whether the successive references are repeated or not. When operating in the DFFC mode, a new iterative parameter tuning algorithm is proposed to enable the performance enhancement compared to the pre-existing DFFC and overcome the limitation of the allowable reference variations. Furthermore, an unbiased estimate method for the convolution matrix of the (process) sensitivity function is developed based on the impulse response experiment. No parametric model is required throughout the proposed DSFC procedure, and the optimal parameters of the DFFC mode can be unbiasedly estimated. Experimental results on an ultraprecision wafer stage confirm that the proposed DSFC combines advantages of ILC and DFFC, and achieves high performance for both repeating and varying tasks. [ABSTRACT FROM AUTHOR]

In precision motion control, well-designed feedforward control can effectively compensate the reference-induced tracking error. To achieve excellent tracking performance such as nanometer accuracy regardless of reference variations, an integrated model-data-based zero-phase error tracking feedforward control (ZPETFC) strategy is synthesized for precision motion systems with complex and nonminimum phase (NMP) dynamics. The feedforward controller comprises a conventional ZPETFC controller and a gain compensation filter structured with symmetric finite impulse response (FIR) filter. Especially, the conventional ZPETFC is predesigned based on the plant model, and consequently, the feedforward controller is parameterized by the gain compensation filter coefficients, which results in excellent capacity for approximating the inverse behavior of the complex and NMP dynamics. In order to compensate the modeling error in the conventional ZPETFC design and improve the tracking performance, a data-based instrumental-variable method with impulse response experiment is developed to obtain the optimal parameter vector under the existence of noise and disturbances. Furthermore, the ridge estimate method using singular value decomposition is employed to guarantee a fast convergent iteration in the case of ill-conditioned Hessian matrix. The proposed ZPETFC strategy enables a convex optimization procedure with the inherent stability in the iterative tuning process, and is finally implemented on a developed ultraprecision wafer stage. Comparative experimental results demonstrate that the strategy is insensitive to reference variations in comparison with iterative learning control, and outperforms preexisting model-based ZPETFC and data-based FIR feedforward control. [ABSTRACT FROM PUBLISHER]

Iterative learning control is known as an effective technique for improving the performance of systems that require repetitive work. In general, a linear time-invariant low-pass Q-filter is employed to provide robustness to modeling errors and model uncertainties, as well as to suppress the noise transmission in the learning process. A lower bandwidth of Q-filter results in less noise amplification but at the cost of decreasing the learning performance. The filter’s bandwidth thus forms a fixed trade-off between attenuation of repetitive errors and amplification of noise. Although linear time-varying Q-filter in finite impulse response type has been proposed, the performance is still unsatisfactory for the filter of this type has a low attenuation rate in high frequency and is unable to obtain a low bandwidth as well. Therefore, in this article, a linear time-varying Q-filter in infinite impulse response type is put forward, providing a better means to deal with the trade-off. To demonstrate that, experiments have been conducted on the developed dual-stage actuated wafer stage, which consists of a short-stroke stage for accurate positioning and a long-stroke stage for coarse positioning. The results illustrate that the proposed method results in a significant improvement in tracking performance, which includes lower converged error and decreased settling time. [ABSTRACT FROM PUBLISHER]

The planar motion stage with multiple degrees of freedom, positioning in large travel ranges, has been widely used in high-accuracy applications such as semiconductor fabrication equipment and advanced scientific instruments. To achieve precision motion control, iterative learning control has been regarded as an effective means. However, linear iterative learning control techniques attenuate recurring disturbances while amplifying the non-recurring, suffering a fixed trade-off between convergence rates and noise amplification. In the present paper, an amplitude-based nonlinear iterative learning control is proposed with learning gain continuously updated to improve the control performance of the planar motion stage. For error levels beyond a predefined threshold, additional learning gain will be effectively used to diminish the low-frequency tracking error. Below the threshold, the original low gain value is maintained to avoid high-frequency noise amplification. Performance assessment on the developed non-contact planar motion stage shows that the amplitude-based nonlinear iterative learning strategy can realize a remarkable performance which includes micrometer positioning over large travel ranges, and provides a more desirable means to deal with the convergence rate and noise amplification. [ABSTRACT FROM AUTHOR]