In this article, the ultrahigh-precision planar motion control problem is investigated for a novel long-stroke multileaf spring based microstage, by considering the cross-axial coupling-induced varying stiffness. In particular, a discrete-time dynamical model with the stress stiffening effect is presented in a parameter varying form, where a discrete-time adaptive extended state observer (ESO) with exponential forgetting factor is developed based on Kalman-like observer method, and a synergetic control theory (SCT) approach is further proposed combining the observer structure to achieve microscale multiaxis motion control. The conditions on the convergence of the adaptive ESO and the robust stability of the closed-loop system are also derived with a theoretical analysis. Finally, the proposed control architecture is implemented (in real time) on a designed microstage, demonstrating significant performance improvement over existing results such as proportional-integral-derivative (PID) control and conventional active disturbance rejection controllers control methods.

Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides us rich information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel ridge regression based machine learning is a promising tool to interpret pressure transients from a single PDG. Kernel ridge regression denoises and deconvolves the pressure signal efficiently and recovers the full reservoir behaviors. In this work, the machine learning framework was extended to two applications: multiwell testing and flow rate reconstruction. The multiwell testing was formulated into the machine learning algorithm using a feature-coefficient-target model. The features were nonlinear functions of flow rate histories of all the wells. For each well, the features and the pressure target of this well were used to train its coefficients, which implicitly contain the information about the reservoir model and well interactions. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model. The multiwell machine learning model was demonstrated to be useful in several different ways, including artificial interference testing and reservoir pressure prediction at various well locations based on flow rate data only. Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. A set of new features were developed as functions of pressure to model the flow rate. Coupled with kernel ridge regression, the developed features were tested on both synthetic and real data sets and demonstrated high prediction accuracy. The success of the rate reconstruction modeling also illustrates the flexibility of machine learning to different kinds of modeling, by adapting features and targets. The models for both applications maintained the advantages of the machine learning based single well pressure interpretation in terms of the accuracy of prediction, computational efficiency and tolerance to noise. This work further demonstrates machine learning as a promising technique for PDG data analysis.

To further enhance the performance of the fast tool servo (FTS) system in terms of stroke, load capacity, and application area, this paper proposes a novel fast tool servo device driven by a voice coil motor (VCM), based on a three-segment uniform corrugated flexure (CF) guiding mechanism, with a large stroke, high accuracy, and high dynamics. To describe the unified static characteristics of such device, the compliance matrix method is applied to establish its model, where the influence of CF beam structural parameters on the FTS device is investigated in detail. Furthermore, resolution and positioning accuracy tests are conducted to validate the features of the system. The testing results indicate that the maximum stroke of the FTS device is up to 3.5 mm and the positioning resolution values are 3.6 μm and 2.4 μm for positive and negative stroke, respectively, which further verifies the device’s effectiveness and promising application prospect in ultra-precision microstructure machining.