Iterative feedback tuning (IFT) enables the data-based optimization of feedback controller parameters without the plant model and disturbance model. However, the traditional IFT suffers from a generally slow convergence rate and requires multiple experiments in each iteration, which severely limit its applicability for precision motion industry. In this paper, a new framework for IFT with its focus on the practical applicability is synthesized. Specifically, in order to improve the convergence rate, a novel two-loop iterative algorithm of IFT is proposed by introducing a weighted gradient of the performance criterion. This algorithm seeks to directly minimize the performance criterion instead of its linear approximation in each iteration. Furthermore, an unbiased estimate method of the auxiliary variables is developed based on the impulse response experiment. These guarantee the proposed approach high convergence rate with less experiments required per iteration. Comparative simulation and experimental results demonstrate that the proposed approach converges much faster than the traditional IFT, and outperforms the model-based approach in terms of the tracking performance. The ease of implementation and effectiveness makes the proposed approach highly suitable for industrial applications.

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.

The aim of this paper is to propose a novel rational feedforward tuning (RFT) method, by directly mapping the feedforward signal learned by dual-loop iterative learning control (DILC) onto the corresponding reference, that achieves high performance for varying trajectory tracking tasks. The DILC algorithm is first developed by paralleling the standard iterative learning control (ILC) with an additional iterative loop. Different from the standard ILC, DILC can learn an ideal feedforward signal eliminating the reference-induced error even though a robustness filter presents for the robust convergence against model uncertainties. Then, based on the reference and the feedforward signal learned by DILC, an instrumental variable (IV)-based algorithm is developed for the parameter tuning of the rational feedforward controller, which leads to unbiased estimates and optimal accuracy in terms of variance. The proposed method combines the performance of DILC with the flexibility of rational feedforward controllers. Comparative simulation and application to an ultraprecision wafer stage illustrate the enhanced performance of the proposed approach compared to pre-existing results.

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.

Decoupling control is still an important research topic for precision multiple-input-multiple-output (MIMO) motion systems involved in computer numerically controlled (CNC) machine tools, wafer scanners, etc. In this paper, to minimize internal coupling and improve servo performance, a data-based iterative dynamic decoupling control (IDDC) approach is synthesized. Specifically, a MIMO dynamic decoupling controller structured with finite impulse response filter is used as an add-on to a static decoupling part. Then, a data-based parameter optimization algorithm is developed such that the optimal parameters can be iteratively solved based entirely on the input/output data by minimizing the coupling-induced error. Unlike pre-existing IDDC approaches, the proposed approach can achieve an unbiased estimate of the optimal parameters combined with a small estimate variance that is illustrated through numerical simulation. Finally, application to an ultraprecision wafer stage confirms that the proposed approach significantly decreases the coupling-induced error and achieves enhanced performance compared to pre-existing approaches.