The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods can not achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this paper develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials.

The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on a fast recursive algorithm (IFFT-FRA) is developed in this article. Explicitly, based on FRA, the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in the presence of noise. Comparative experiments on a linear motor confirm the effectiveness of the proposed approach.

Fault detection is crucial for ensuring the reliability and safety of industrial processes, as it helps prevent equipment failures and minimizes downtime. It has wide applications in industrial processes and machine intelligence but remains unsolved problems. Major challenges include the variations in data distribution and temporal dynamics due to the nonstationary nature, which is driven by factors such as frequent switching between operational modes, aging of distributed control sensors, and environmental variability. Traditional fault detectors are suboptimal, while nonstationary-aided methods easily make biased detections towards the multiple complex nonstationary components. In this paper, we present a new fault detection model for nonstationary processes through multiscale content learning to disentangle feature representations (MCD). MCD consists of three sub-modules: Diverse Content Module (DCM), Stationary and Nonstationary Disentanglement Module (SNDM), and Component Reweighting and Reconstruction Module (CR²M). DCM enriches multiple representations via multiscale modeling of network informational content. SNDM disentangles the homogeneous stationary and heterogeneous nonstationary (S-N) features from multiple local representations, which is formulated as an encoding-decoding-encoding process with a skip disentanglement and min-max constraint. CR²M further fuses local S-N features with an adaptive learnable reweighting strategy, achieving a fine-grained reallocation of the importance of global S-N attributes. Experimental results on a numerical process and a real-world process demonstrate the superiority of MCD in fault detection for nonstationary processes.

Permanent magnet linear motors (PMLMs) are gaining increasing interest in ultra‐precision and long stroke motion stage, such as reticle and wafer stage of scanner for semiconductor lithography. However, the performances of PMLM are greatly affected by inherent force ripple. A number of compensation methods have been studied to solve its influence to the system precision. However, aiming at some application, the system characteristics limit the design of controller. In this paper, a new compensation strategy based on the inverse model iterative learning control and robust disturbance observer is proposed to suppress the influence of force ripple. The proposed compensation method makes fully use of not only achievable high tracking accuracy of the inverse model iterative learning control but also the higher robustness and better iterative learning speed by using robust disturbance observer. Simulation and experiments verify effectiveness and superiority of the proposed method.

For the lightweight ultraprecision motion stages, suppression of the vibration caused by the flexible modes is of increasing importance in the modern manufacturing industry. The overactuation scheme attracts more interest since it is capable of both effectively suppressing the vibration and significantly improving the motion performance. However, the nonsquare systems, where the number of actuators is larger than the number of rigid modes, generally lead to a nonunique decoupling for the control system, which is actually intractable. In this article, a novel dynamic decoupling approach based on the idea of command shaping is presented for the overactuated motion systems, where better suppression of vibration caused by all the controllable flexible modes can be achieved with limited redundancy of actuators. The proposed decoupling approach is illustrated through the experiments on a validation prototype stage.