Series Snake robots possess strong adaptability for unstructured environments, but their trajectory tracking control is hindered by nonlinear dynamics and model uncertainties. This article proposes an optimal tracking control strategy based on an actor–critic reinforcement learning framework. The method integrates line-of-sight guidance with serpentine gait generation, while a neural network identification system approximates the solution of the Hamilton–Jacobi–Bellman equation for unknown dynamics. Actor and critic networks are employed to update control policies and cost functions online, reducing dependence on precise models. Rigorous theoretical analysis proves that position and velocity errors achieve semi-global uniform ultimate boundedness. Both simulations and prototype experiments were conducted based on a servo-driven yaw-pitch linkage alternating series snake robot. The results verify that the proposed method can achieve accurate trajectory tracking, rapid convergence, and stable joint control, demonstrating its effectiveness and superiority compared to existing methods.

Cognitive state detection holds significant research value in the field of human–computer interaction and neural engineering. However, existing works are insufficient in modeling the temporal dynamics of multimodal physiological signals, which leads to heterogeneous distribution differences in cross-modal feature interactions. In addition, domain shift issues under cross-subject and few-sample conditions restrict the model generalization performance. To cope with these problems, this work proposes a cognitive state detection framework that integrates Transformer-based multimodal feature interaction and self-training of pseudolabel optimization. First, the multihead attention mechanism is introduced to model the temporal evolution patterns across modalities, dynamically harmonizing cross-modal contributions to extract cognitive state-related shared features. Then, a dual-model cross-validation strategy is designed to filter high-quality pseudolabeled samples from the target domain for subsequent self-training, effectively avoiding the dependency on auxiliary modules in domain adaptation. Finally, Extensive experiments show that the proposed work significantly improves the recognition accuracy, and the designed pseudolabel optimization mechanism can be transferred to related tasks without increasing model complexity.

Absolute testing is a key technique for achieving nanoscale measurements in Fizeau interferometry. Traditional methods use Zernike polynomials to fit the test surface form and indirectly infer the reference surface form, which can lead to error accumulation. In this article, we propose a neural absolute testing method that uses an advanced neural radiance field to directly model the reference surface form, thereby reducing error accumulation. The proposed framework includes an invariant surface branch and a variant surface branch; the sum of these two branches is expected to closely approximate the measured data under varying rotations and shifts. Consequently, a self-supervised training strategy has been developed to optimize the model parameters, eliminating the need for extensive training datasets with ground truth labels. Synthetic experiments demonstrate that the proposed method achieves superior absolute calibration accuracy compared to the traditional Zernike polynomial approach. Furthermore, real-world experiments performed using a Fizeau interferometer confirm that the proposed method produces a more robust reference surface form than the classical Zernike polynomial technique.

Due to the uniform sampling pattern and concise trajectory generation, the raster scan has become the mainstream scan mode for atomic force microscopes (AFMs). However, the high-frequency components of the triangular trajectory and the stair trajectory for generating the raster scan tend to motivate the lightly damped resonance of the nano-positioner insides AFMs and thus severely limit the imaging speed of AFMs. To handle this issue, a novel smoothed raster scan trajectory generation method is proposed in this article via replacing the non-imaging path of both fast axis and slow axis with an optimally designed smoothed transition trajectory. A universal mathematical expression of the smoothed raster scan trajectory is developed, following by the detailed frequency spectral analysis. The analysis results reveal that the inner model of the smoothed slow-axis trajectory is the sum of two cascaded integrators and a series of sinusoidal internal models distributed at the scanning frequency along with its harmonic frequencies. As a result, an embedded repetitive control (RC) scheme is adopted to achieve the high-bandwidth and high-accuracy tracking for both fast- and slow-axis trajectories so as to contribute to a high-speed raster scanning. Comprehensive trajectory tracking experiments and imaging experiments are performed on a commercial AFM. The experimental results show that, under the scanning frequency of 100 Hz, the root-mean-square tracking error of the proposed smoothed trajectory is reduced from 47.1 nm (fast axis) and 7.5 nm (slow axis) of the conventional trajectory to 6.9 nm (fast axis) and 3.8 nm (slow axis) with the same RC schemes, and maximum tracking error of the same smoothed slow-axis trajectory is reduced from 387.7 nm with PI scheme and 138.2 nm with PI+PDOB scheme to 13.0 nm with the RC scheme, which verify the effectiveness and advancement of the proposed trajectory smoothing method and the developed control scheme. Note to Practitioners—Raster trajectory implemented via fast- and slow-axis coordinated motion has been widely used within modern precision manufacturing and measuring systems such as the atomic force microscope, coordinate measuring machine, laser or ion-beam machine tool. The high-accuracy and high-speed tracking of the raster trajectory is the basic requirement for these sophisticated systems to achieve the intended function. However, the trajectory mutation of the raster trajectory induces large amounts of high-frequency trajectory components for each servo axis and thus brings a significant challenge for its high-performance motion control. In this article, a novel smoothed raster scan trajectory generation method is developed to reduce the high-frequency components of the conventional raster trajectory and thus facilitate its trackable performance on the basis of the frequency spectral analysis of the smoothed raster scan trajectory. The spectral analysis results reveal for the first time that the frequency spectral function of the smoothed slow-axis trajectory is the linear combination of those of two cascaded integrator and a periodic trajectory. This conclusion motivates the usage of a simple yet efficient embedded repetitive control (RC) scheme by integrating a proportional-integral (PI) controller with a repetitive controller for the high-performance tracking of both fast and slow axes whilst suppressing their cross-coupling errors without extra compensators. The effectiveness of the proposed smoothed raster scan trajectory as well as the developed embedded RC scheme have been preliminarily verified via high-speed AFM imaging. This development can also be applied on other systems involved in the high-speed tracking of the raster trajectory.

The dual-loop controller (DLC) with both inner damping and outer tracking controllers has demonstrated exceptional performance in high-speed control of piezo-actuated nano-positioners. However, the accurate low-order model of the plant is imperative for designing the damping controller, according to the conventional DLC design principle. This limits its application in controlling piezoelectric tube scanners (PTSs) with compound dynamics. To handle this problem, this study introduces a frequency domain method for designing the DLC based on the frequency response data of the PTS. This method mitigates issues related to modeling errors. Specifically, the Nyquist diagram is employed to provide the stability boundary for parameters determination. A constraint optimization problem is formulated to achieve a high bandwidth with a flat amplitude frequency response. And the differential evolution algorithm is then adopted to find an optimal solution. Experimental validation on a PTS comfirms the effectiveness of this frequency domain design method. The results indicate that the control bandwidth of the optimized DLC achieves 848 Hz for a PTS with the first resonant frequency of 702 Hz. The superiorities of the DLC designed by the proposed optimization method are also validated via comparative tracking experiments involving step and triangular trajectories.Note to Practitioners—Frequency response data (FRD)-based methods open a new perspective to the controller design and optimization. Unlike traditional methods relying on transfer function models, the FRD of the system is directly identified and employed for the controller design and optimization, eliminating the need for extensive modeling and system identification efforts, thereby reducing errors. This paper proposes an FRD-based method for the simultaneous optimization of the DLC to achieve a high control bandwidth with a flat amplitude frequency response. This method relieves the requisite of accurate low-order model of the plant in conventional DLC designs. A closed-loop control bandwidth that exceeds the first resonant frequency is obtained for a PTS with complex high-order dynamic models. The proposed method enriches the scope of the DLC for the high bandwidth control, extending its applications to the systems with compound dynamics, which is significant to the high-speed control tasks of nano-positioners, such as the atomic force microscope imaging.

Given a set of calibrated images, Multiple View Stereo (MVS) applies end-to-end depth inference network to recover scene structure. However, previous methods designed pixel-visibility modules to aggregate cross-view cost, ignoring the consistency assumption of 2D contextual features in the 3D depth direction. The current multi-stage depth inference model also relies on intensive depth samples, which requires high memory consumption. To alleviate these problems, this work exploits edge-assisted epipolar Transformer for multi-view depth inference. The improvements of this work are summarized as follows: 1) The epipolar Transformer block is developed for reliable cross-view cost aggregation, and the edge detection branch is designed to constrain the consistency of epipolar geometry and edge features. 2) The dynamic depth range sampling mechanism based on probability volume is adopted to improve the accuracy of uncertain areas. Comprehensive comparisons with the state-of-the-art works indicate that our work can reconstruct dense scene representations with limited memory bottleblockNote to Practitioners—Learning-based MVS can obtain dense point clouds with accurate depth map estimation, which are widely applied in the fields of unmanned driving, battlefield environment perception and robot navigation. MVS-based scene reconstruction technology is the premise of the subsequent planning, decision-making and control of the human-machine system. To obtain dense scene representation with limited memory and runtime, this work proposes a multi-view stereo network with edge-assisted epipolar Transformer. Experiments on public benchmarks verify the feasibility and effectiveness of our model, which has good potential in battlefield environment reconstruction and human-computer interaction fields, and can provide intuitive and dense scene representation for decision-making assistance.

The normal-stressed electromagnetic actuators (NSEAs) have emerged as a promising actuation technology for developing high-performance compliant nano-positioning stages. But less attention was devoted to the modeling of the multi degrees-of-freedom (DoF) NSEA-based stages. This paper aims to improve the modeling accuracy of the stages’ static and dynamic performances by introducing a novel electromagnetic-mechanical modeling method. Unlike the previous studies, the proposed modeling method considers the effects of the NSEAs’ negative stiffnesses along both its actuation direction and the vertical-to-actuation direction in the electromagnetic modeling step. Together with the model of the mechanisms, the coupled electromagnetic-mechanical model is analytically derived. As an application case, the working stroke and resonant frequency of a NSEA-based parallel-kinematic 2-DoF compliant nano-positioning stage are evaluated with the proposed model. It is demonstrated by numerical, simulation, and experimental studies, that the proposed modeling method is accurate for predicting the performances of the NSEA-based nano-positioning stages. This is significant for the future development and applications of NSEA-based mechatronic systems.Note to Practitioners—The multi-DoF NSEA-based nano-positioning stages are the promising choice to develop long stroke, high natural frequency nano-positioning stages. However, some inherent electromagnetic-mechanical coupling effects are ignored in previous study when modeling the multi-DoF NSEA-based stages, which leads to a significant prediction error. By considering the electromagnetic characteristics both along and vertical to actuation direction, this study proposed an electromagnetic-mechanical model for the 2-DoF NSEA-based stages. The proposed model shows a higher accuracy to evaluate the static and dynamic performances of the NSEA-based stages, which are beneficial to practical applications such as trajectories tracking.

Planar XY scanners are essential for scanning probe microscopes. To overcome limitations in current piezo-actuated scanners, a novel normal-stressed electromagnetic scanner named MagScanner is introduced to scan a chromatic confocal sensor probe and construct a non-contact scanning probe-based surface profilometer. The MagScanner has a monolithic armature to provide dual-axial decoupled driving forces and a set of dual-axial compliant bearings to guarantee the decoupled planar scans. To properly select the system parameters for satisfying the design target, analytical models and finite element simulations are combined for the electromagnetic and mechanical components of the MagScanner. The experiment test on the prototype shows a stroke of$ \pm 200~ \mu $m and a natural frequency of around 220 Hz for each axis, and the mutual dynamic cross-talks are smaller than 36.34 dB. Furthermore, by adopting the internal model control principle, the prototype precisely follows the Lissajous scanning trajectory with a peak-to-valley tracking error of less than 0.37%. After calibrating the error motion-induced measurement errors, the single-shot sub-aperture measurement of a typical micro-structured surface is demonstrated to have a peak-to-valley form error within$ \pm ~60 $nm, which is further verified by applying for the measurement of a large-area micro-structured surface. Note to Practitioners—This work was motivated by developing a probe-based surface profilometer for measuring micro-structured surfaces. Although a large scanning area with high frequency was essential to achieve efficient surface scanning, it was challenging for conventional piezoelectric scanners owing to the inherently limited strain rate of piezoelectric ceramics. Hence, we developed a novel planar XY scanner based on normal-stressed electromagnetic actuation, which is promising for implementing dual-axial direct-drive scanning in hundreds of micrometers with an outperforming natural frequency.

This article proposes a robotic fish formation control scheme that is based on finite-time terminal sliding mode to achieve coordinated tracking of multiple target paths under external disturbances and internal parameter perturbations. The method explores the lateral sliding mechanism of each body in the surge and sway directions by constructing a directional compensation guidance strategy that is based on a finite-time disturbance observer, thus enabling the precise coordinated movement of multiple robotic fish. Furthermore, this article acknowledges that the highly coupled dynamics of the robotic fish are susceptible to external environmental influences and modeling accuracy. Hence, a rapid global terminal sliding mode fuzzy controller that considers tangential displacement is introduced, and fuzzy adaptive methods are utilized to fit complex uncertainties. This approach mitigates the chattering problems commonly associated with traditional sliding mode control and enhances the error convergence speed and accuracy of the robotic fish formation system.

The achievable performance with repetitive control is limited due to its inherent sensitivity to the frequency shift away from the intended periodic frequencies and the undesired gain amplification of the aperiodic disturbances. To address these limitations, the paper proposed a general parametric repetitive control (GPRC) method based on the IIR filter with the features of low-order and excellent magnitude responses to result in better tracking performance in diverse applications. By analyzing its sensitivity function, it is found that the design of GPRC can be converted to the explicit parametric design of an IIR high pass filter. The controller design process and the stability analysis are presented in detail. To show the effectiveness of GPRC, comparative experiments are conducted via tracking sinusoids, triangular trajectories and other complex trajectories with multi-frequency components. The experimental results show that, in contrast with the conventional repetitive control (CRC) and a modified repetitive control (MRC), the GPRC exhibits excellent robustness against the frequency shift and advanced performance at the aperiodic frequencies. The tracking results of the sinusoids show that the maximum tracking error obtained with GPRC for a frequency shift of 3 Hz decreases from$0.0259~\mu m$(CRC) and$0.2207~\mu m$(MRC) to$0.0101~\mu m$at the nominal frequency of 1000 Hz, demonstrating the merits of the proposed GPRC. Note to Practitioners—To enable automation systems, one of the crucial requirements is to track repetitive references with high precision. Although the normal repetitive control (RC) based schemes are successfully applied to improve the tracking accuracy of periodic trajectories, the existing RC schemes suffer from the problems of lower robustness against frequency shift and the unwanted gain amplification at the aperiodic frequencies due to Bode’s sensitivity integral. To overcome this problem, this paper proposes a novel general parametric repetitive control (GPRC) method via characterizing the loop properties quantitatively based on the IIR high pass filter design. Focusing on specific issues, the detailed variations to handle the errors at only the odd- and even-harmonics are also demonstrated. This framework leads to a flexible solution in practical implementations. The experimental validation on a piezo-actuated nanopositioning stage is comparatively presented in terms of tracking accuracy and rejection ability of the gain amplification at the aperiodic frequencies. With its flexibility and effectiveness, the proposed GPRC can be easily implemented in diverse applications.

This article presents a novel high-gain loop-shaping (HGLS) method for precision motion devices by introducing high gains into the control loop. The high gains can be generated via the inclusion of a low pass filter in a feedback manner, which could significantly improve the tracking accuracy for general trajectories with a simple structure. This control law also endows the superiority of robustness against model variations and external disturbances. To evaluate the performance of the proposed HGLS method, tracking and disturbance rejection experiments are conducted on a custom-designed piezo-actuated nanopositioner. The experimental results demonstrate the advancement of HGLS in terms of precision motion control, where the root-mean-squared error is reduced from 7.8 nm to 1.8 nm as compared with the high-gain proportional-integral controller with the same phase margin when tracking a random Non-Uniform Rational B-splines curve with large external disturbances. With its remarkable advantages of high tracking accuracy and simple structure, this method offers a practical solution for industrial automation applications for enhancing the control performance.Note to Practitioners—Advanced control methods are of great importance in meeting the tremendous requirements of automation in mechanical and electronic systems. Nonetheless, it is still challenging for a controller to simultaneously realize high control accuracy and strong robustness for general trajectories while retaining a simple structure. In this paper, a novel HGLS method is proposed. For this method, high control accuracy and strong robustness can be achieved by introducing high gains into the control loop, which can be generated by the inclusion of a simple low pass inside the closed-loop plant. A trade-off between the control gain and stability margin can be balanced only by tuning the parameters of the employed low-pass filter, and thus the implementation of the HGLS is rather simple. This method has no constraints on the form or order of the plant models and holds robustness against model variations. As compared with the high-gain proportionalintegral controller with the same phase margin, the control gain of the HGLS is much higher within the effective control bandwidth. Therefore, outstanding control performance can be achieved for general trajectories within this bandwidth. Due to the strong universality and simple structure of the HGLS, it is promising to be implemented in various industrial automation equipment, such as machine tools, multi-axis systems, robotics, scanners, etc.

Surface texture measurement helps control the quality of large workpieces produced by machine systems. Current optical measurement methods, e.g., fringe projection profilometry and coherent scanning interferometry, are difficult to perform full-field measurements of microtextures on large-sized surfaces. Photometric stereo could potentially address this challenge; however, machined metal surfaces exhibit highly reflective non-Lambertian reflectance that dramatically decreases its effectiveness. To solve this problem, a task-specific near-field photometric stereo approach is proposed to dramatically enhance the accuracy of the surface normal estimation on machined metal surfaces. First, a near-field photometric stereo network is designed for efficient industrial applications, which fully employs the pixelwise information under a small number of lights to achieve surface normal estimation. Then, a task-specific training strategy is proposed to train the proposed network, where a task-specific real dataset is established for each specific combination of material and machine processing to optimize the network parameters initially trained by a synthetic dataset. Experiments on synthetic sinusoidal surfaces and real machined surfaces validated the superiority of the proposed method for metal reflectance compared with the state-of-the-art photometric stereo methods and the sub/micrometer-level sensitivity to surface height variations. Two case studies on tool marks on a large freeform surface and defects on a stamping surface are presented, demonstrating their potential for industrial applications.

This work presents a butterfly spiral propulsion mode of snake robots to realize the tracking control on the objective trajectory in multiple scenarios. This method investigates the force mechanism of each body element in the yaw and pitch directions. The butterfly spiral gait mechanic and friction models are constructed to offset the lateral torque force caused by joint rotation. In addition, this work combines an integral part of improving the line of sight guidance scheme, which eliminates the robot's sideslip when tracking the curve track and enhances the body's adaptability to different scenarios. Lyapunov's theory proves the stability of the designed guidance strategy. Simulation and experimental results illustrate that the designed butterfly spiral gait and guidance scheme can provide the snake robot faster tracking results and more stable error performance than the cylindrical and conical spiral gait.

The highlight reflectance on machined metal surfaces dramatically influences the performance of optical measurement sensors, such as fringe projector profilometers. Previous research addressed such an issue through fusion of images under different camera exposure times or modulation of the lighting intensity of the projector. Differently, this article provides a new research perspective that resorts to the modeling of complicated anisotropic reflectance on machined metal surfaces, which essentially causes the decrease in the quality of fringe images. In this article, an anisotropic neural reflectance model is proposed for the effective description of machined metal reflectance. Then, a facility with one camera, one projector, and several lighting emittance didoes is designed for the application of the proposed model in the measurement of curved machined metal surfaces. A complete and accurate point cloud is finally obtained through fitting of the real reflectance using the proposed model in a self-supervision way. Synthetic experiments illustrate that the capability of the proposed model to describe the machined metal reflectance is considerably enhanced compared with the traditional parameterized reflectance model and isotropic neural reflectance model. Real experiments prove that the proposed method can achieve measurement accuracy of 23 μm on machined metal surfaces with repeated accuracy of 0.15 μm, where the ground truth is given by the coordinate measuring machine.

To ensure the machining quality of micro/nano scale structural units for meter scale workpieces, relay turning with multiple single-point diamond tools has been broadly required. However, the existing tool setting methods have the problems of long tool setting time and low tool setting accuracy. To address the above issues, a novel normalized variational auto-encoder model with an adaptive activation function (NVAE-AAF) is proposed in this article. The batch normalization and the adaptive activation function are introduced into the variational auto-encoder model to learn robust features of force signals at the tool idle move state. Then, the reconstruction error threshold is constructed according to the kernel density estimation method to realize the nanoscale tool setting. In the ultraprecision tool setting experiments based on piezoelectric ceramic force sensing, the reconstruction error of the force signals at the tool idle move state is less than 0.07%, and the contact detection accuracy reached 92%. Compared to the traditional trial cutting for tool setting method, the proposed method significantly improves tool setting accuracy by 75%–85%, reaching a level of 75 nm.

State of health (SOH) estimation of lithium-ion batteries (LIBs) is of critical importance for battery management systems (BMSs) of electronic devices. An accurate SOH estimation is still a challenging problem limited by diverse usage conditions between training and testing LIBs. To tackle this problem, this article proposes a transfer learning-based method for personalized SOH estimation of a new battery. More specifically, a convolutional neural network (CNN) combined with an improved domain adaptation method is used to construct an SOH estimation model, where the CNN is used to automatically extract features from raw charging voltage trajectories, while the domain adaptation method named maximum mean discrepancy (MMD) is adopted to reduce the distribution difference between training and testing battery data. This article extends MMD from classification tasks to regression tasks, which can therefore be used for SOH estimation. Three different datasets with different charging policies, discharging policies, and ambient temperatures are used to validate the effectiveness and generalizability of the proposed method. The superiority of the proposed SOH estimation method is demonstrated through the comparison with direct model training using state-of-the-art machine learning methods and several other domain adaptation approaches. The results show that the proposed transfer learning-based method has wide generalizability as well as a positive precision improvement.

This article presents a data-driven, Koopman operator-based modeling scheme for analyzing and predicting cross-coupling hysteresis effects of the piezoelectric tube scanners (PTSs) used in atomic force microscopes (AFMs). Such cross-coupling hysteresis effects between different PTS axes significantly reduce the positioning precision of AFMs. In contrast to most of the existing methods for PTS hysteresis, which involve complex nonlinear dynamics identification processes, the present study leverages the Koopman operator theory instead to treat the nonlinear hysteresis as a linear system. Therein, a Hankel extended dynamic mode decomposition (H-EDMD) algorithm is proposed to learn the finite-dimensional descriptions of the Koopman operator and the associated Koopman eigenspectrum. Moreover, the proposed H-EDMD even allows sparse sampling on the PTS systems, which is desirable in real industrial applications. Finally, extensive comparison experiments with a mainstream modified Prandtl-Ishlinskii model are conducted on an NTMDT Prima AFM to substantiate the effectiveness and superiority of the proposed H-EDMD method.

Repetitive disturbances exist widely within the automation systems, which is one of the major issues that hinder the achievement of precision operations. Dedicated to mitigating these disturbances, a generalized periodic-disturbance observer (PDOB) using spectrum-selection filtering scheme is proposed in this paper with an application to a non-minimum phase system. The design process of the spectrum-selection filter that derives from the comb-like notch filter for the proposed PDOB is presented in detail. The variants of the proposed PDOB are also presented for the disturbances distributed only in odd- and even-harmonics. To achieve tracking of the desired trajectories, the proposed PDOB is combined in parallel with a baseline Proportional-Integral (PI) controller. The stability condition of the closed-loop system is derived to provide criteria for parameters selection. The experimental validation of the generalized PI+PDOB is conducted via real-time cross-coupling suppression in raster scanning of atomic force microscope (AFM), where the coupling induced root-mean-square tracking errors are reduced from 141.8$nm$to 2.4$nm$by employing the proposed PDOB for the scanning rate of 100 Hz. The results of convergence testing, raster scanning and AFM imaging are compared to illustrate the significant improvements achieved with the proposed PDOB. In addition, experimental results show that the tracking performance using PI+PDOB in the case of existing periodic disturbances resembles with that using PI control without periodic disturbances, which implies that the employment of the proposed PDOB does not interfere with the tracking-based control schemes for staircase trajectories, showing the advantages of the proposed PDOB.Note to Practitioners—Repetitive motions facilitate diverse advanced functions within the automation systems, including the scanning electron microscope, atomic force microscope, manipulators, and robotic systems. These operations would unavoidably introduce repetitive disturbances, hindering its achievable precision. One particular issue is the widely existent cross-coupling effect that results in these repetitive disturbances. In this paper, a generalized periodic-disturbance observer (PDOB) based on the spectrum-selection filtering scheme is developed to address the repetitive disturbances for real-time coupling suppression in raster scanning of Atomic Force Microscopy. Consequently, the offline learning procedures required for other learning based schemes can be avoided. Without loss of generalization, the design of PDOB is demonstrated with an application to non-minimum phase systems. A scheme of the varying parameter is also developed to achieve both fast convergence and better performance at rejecting the periodic disturbances. The detailed variations to handle the disturbances distributed only in odd- and even-harmonics are also demonstrated. Different from other disturbance observers, the inclusion of the spectrum-selection filtering scheme provides more flexibility in rejecting specific repetitive disturbances. In terms of tracking accuracy and the simple structure, this development can also be easily implemented to other systems that suffer from repetitive disturbances.

In this paper, a new piezoelectric-actuated biaxial compliant microgripper with long strokes is proposed for automatically gripping and rolling tiny rigid objects. In order to improve the working stroke and maintain a compact footprint, a counter-side distributed two-stage lever amplifier with parallelogram mechanism is introduced. Based on the pseudo-rigid body model, analytical models of the displacement amplification ratio, input stiffness, and natural frequency of the left- and right-sided gripper mechanism are established. Structural optimization and performance simulation of the proposed microgripper mechanism are carried out with finite element analysis simulation. A prototype microgripper has been fabricated for open-loop and closed-loop tests to verify its working capabilities. The clamping and rubbing experiments show that the designed microgripper can grasp and rub an optical fiber with the diameter of 200$\mu \text{m}$for rolling over 45°. The developed microgripper has a promising application in precision micromanipulation fields such as optical fiber alignment. Note to Practitioners—This work is motivated by the requirement of designing a biaxial microgripper with both a large working stroke and compact structure for complex operation in optical fiber alignment. Through a series of open-loop and closed-loop experiments on the developed prototype, it is demonstrated that the proposed dual-axis microgripper has superior working performance. The clamping stroke and rubbing stroke of the gripper are$251.2~\mu \text{m}$and$225.0~\mu \text{m}$, respectively. The first two natural frequencies of the gripper are 350.63 Hz and 603.37 Hz, which correspond to the working modes of the right-side and left-side gripper mechanisms, respectively. The closed-loop experimental results show that the resolution of output displacement of the gripper is close to$1.2~\mu \text{m}$and the resolution of the clamping force is 3 mN. As compared with the reported microgrippers in previous work, the designed mechanism exhibits both a large working stroke and high resonant frequency for ensuring the reliability and rapidity of micromanipulation task.

Three-dimensional spiral gait enables a snake robot to climb over obstacles, cross caves, and adapt to complex environments. This article reports an antidisturbance path-following control method for a snake robot with a spiral gait. This method reduces the deviation of the robot's position in following the ideal path by estimating the time-varying parameters, the external disturbances, and the viscous friction coefficients. The estimations are used to compensate for the control inputs of the system, which can improve the adaptability of the robot to the environment. Then, the attitude and position errors can rapidly converge to the origin. An appropriate Lyapunov function is adopted to explore the stability of following errors. Experimental results show that the proposed method can accelerate the convergence rate of errors, reduce the fluctuation peak, and improve the following stability of snake robots.

In this article, an intelligent feedforward prediction and compensation scheme to combine with a dual-loop high-bandwidth controller is proposed for high-speed and high-precision tracking controls of a nanopositioning stage. First, the dual-loop controller consisting of an inner loop damping and an outer loop tracking controller is developed with all the parameters optimized simultaneously, which could provide a control bandwidth over the first resonant frequency of the stage. Next, the Gaussian process machine learning model is employed to capture the dynamic characteristics of the tracking error of the dual-loop controlled plant. Then, a feedforward compensator is constructed to add a compensation term to the initial reference trajectory. Experimental investigations on a self-made piezoelectric-actuated stage validate the effectiveness of the intelligent tracking error prediction method and the excellent performance of the control strategy for high-precision tracking of high-frequency reference trajectories.

Redundant robot manipulators play a significant role in modern industry. In this article, we propose a solution scheme to the trajectory tracking problem of the redundant robot manipulator with physical constraints through the Zhang neural dynamics method. Such problem is integrated into a time-varying system consisting of time-varying nonlinear equation (TVNE) and time-varying linear inequality (TVLI) and solved online by the varying-parameter Zhang neural dynamics (VPZND) model. It is ensured that the redundant robot manipulator can still perform the tracking task perfectly under the coexistence of time-varying bounded noise and physical constraints. Theoretical analysis proves that this VPZND model also has an explicit fixed convergence time. Numerical experiments confirm the feasibility of our VPZND model for TVLI. The trajectory tracking problem of the redundant robot manipulator with six or three degrees of freedom under the dual influence of physical constraints and noise is perfectly solved by the VPZND model, which is enough to verify its practical value.

This work reports an adaptive path-following controller for a multijoint snake robot (MSR) to improve the adaptability of the robot to the environment. The new strategy estimates the time-varying parameters of the system and the external interference to adjust the motion state of the robot in real time. Estimations are used to compensate for the joint torque of an MSR, thus reducing the fluctuation peak of path-following errors. In addition, this work designs an anti-sideslip line-of-sight (LOS) guidance strategy to avoid the deviation of the direction angle. The method can improve the tracking accuracy of an MSR, and the position errors enable the system to achieve uniformly ultimate boundedness (UUB). The angle errors converge to the origin to achieve stability. Experimental results demonstrate that the novel method can accurately estimate the time-dependent parameters, sideslip, and interference, raise the convergent speed of errors, and reduce the fluctuation peak.

This article reports on the design, modeling, control, and testing of a novel compliant nanopositioning stage driven by a self-developed normal-stressed electromagnetic actuator. To facilitate the parameter selection for the stage to achieve the desired stroke and natural frequency, an analytical model of both the electromagnetic circuit and flexure mechanism is established, which is then systematically verified through finite element analysis. By combining a proportional—integral—differential (PID)-based main controller with a system dynamics inversion-based feedforward compensator, a closed-loop control system for the stage is constructed with the main controller being tuned through Bode’s ideal transfer function-based loop-shaping method. The experimental result shows that a stroke of${\pm } 95 ~\mu \text{m}$and a first natural frequency of 743 Hz are achieved for the designed stage. Finally, taking advantage of the constructed control system, the nanopositioning capability is demonstrated by finely tracking the harmonic and nanostair commands. Note to Practitioners—Piezoelectric actuators (PEAs) and voice coil motors (VCMs) are commonly adopted for the actuation of nanopositioning stages. In general, constrained by the inherent small strain, PEA is more suitable for the generation of dynamic motions in dozens of micrometers. Meanwhile, VCM has a theoretically infinite motion range but a slow response due to its low force density nature. Taking advantage of a newly developed normal-stressed electromagnetic actuator (NSEA), we demonstrate the design, modeling, control, and testing of an NSEA-based nanopositioning stage with a relatively long motion range, high bandwidth, and compact size. Considering the high force density and relatively large motion range only constrained by the effective air gap, the NSEA-based stage is demonstrated herein to be very promising to achieve highly dynamic motions within hundreds of micrometers.

Due to the lightly damped resonance and intrinsic nonlinearities, it is difficult for the piezoactuated nanopositioning stage to realize high-bandwidth and high-accuracy control. To handle these limitations, in this work, a dual-loop control scheme based on state-feedback-based modal method is designed to both actively damp and stiffen the resonant mode and to suppress the effects of nonlinearities of the piezoactuated nanopositioning stage. In this scheme, the state-feedback-based modal controller is first designed in the inner loop to enlarge both the damping ratio and natural frequency of the first resonant mode. Then, a proportional–integral (PI) controller is utilized in the outer loop for eliminating the tracking errors caused by other disturbances and nonlinearities including hysteresis and creep. To maximize the control bandwidth of system under the proposed dual-loop scheme, an optimization method is thus proposed for simultaneously tuning the parameters of the inner and the outer loop controllers. Finally, to validate the proposed dual-loop control scheme, comparative experiments are carried out on a piezoactuated nanopositioning stage. Results demonstrate that the proposed control scheme improves the bandwidth of the system from 497 Hz (with PI control) and 1543 Hz (with a commonly used positive acceleration, velocity, and position damping control and a PI controller) to 6546 Hz, which is 664 Hz larger than the first resonant frequency of the original system, validating the effectiveness of the proposed dual-loop scheme on high-bandwidth control. Note to Practitioners—The demand of high-bandwidth and high-accuracy piezoactuated nanopositioning stages increases rapidly. However, the lightly damped resonance of the mechanism and the intrinsic nonlinearities of the piezoelectric actuator limit the tracking performance of the stage. A dual-loop control structure is adopted in this work to improve the tracking performance of the nanopositioning stage. Different from most of the vibration control methods proposed in the literature which aimed only at improving the damping ratio, a state-feedback-based modal controller is designed in the inner-loop for improving both the damping ratio and the stiffness of the system. This task is realized by re-placing the resonant poles of the system to the optimized location. The outer-loop controller adopts the high-gain PI control for eliminating the tracking errors. More importantly, in order to realize the high-bandwidth and high-accuracy control, a numerical optimization method is proposed for simultaneously tuning the parameters of the controllers in inner and outer loops. The controller design is simple, and it can be applied to other systems with second or higher order in which the first resonant mode dominates the system dynamics.

Although surface with multiple holes (SMHs) is widely used in industrial components, the precise inspection of SMH is a knotty task due to the inefficiency of traditional surface inspection technology. Recently, a five-axis surface sweep scanning approach is emerging and shows great potential to boost the surface inspection efficiency by sensing surface in a continuous sweep scanning way. However, an efficient sweep scanning path should cater to the unique kinematic characteristics of the five-axis inspection system. In this article, we present an approach to automatically generate efficient five-axis sweep scanning paths for inspecting SMH. First, the skeleton of SMH is extracted along with the surface partitioned into several patches, based on which a guiding curve-based sweep scanning path can be defined for each surface patch. By modeling the skeleton of SMH as a directed Euler graph and finding a proper sequence to traverse the graph, a continuous five-axis sweep scanning path is then generated to sweep all patches without any transition among them. The nonsweeping time could be eliminated so that the sweep scanning efficiency is drastically improved in this way. Simulation and physical inspection experiments are conducted on two SMHs, showing that our method significantly outperforms existing approaches, such as the popular zigzag method and the method from the leading commercial software of RENISHAW.Note to Practitioners—This article aims to generate an efficient sweep scanning path of a five-axis coordinate measuring machine (CMM) for inspecting the SMHs of a precision component, such as an engine block or a gearbox cover. Although traditional five-axis CMM path generation methods can be applied to SMH inspection, most of them are designed for the point-by-point inspection mode, which intrinsically suffers from extremely low efficiency. Recently, an emerging five-axis surface sweep scanning technology shows great potential for boosting the efficiency of surface inspection by using a continuous surface sensing stylus mounted on a rapid rotary probe head. The realization of high-speed five-axis surface scanning depends on a proper sweep scanning path which takes advantage of the superior kinematic performance of the rotary probe head as much as possible and accordingly avoids assigning heavy workload to the three linear axes with weaker kinematic performance. However, up to date, only a handful of five-axis sweep scanning path planning methods are reported and mainly focus on the simple and compact surface. Therefore, existing methods are incompetent to generate a suitable sweep scanning path for high-speed inspection of SMH. For these reasons, we present a novel and practical approach to automatically generate a continuous five-axis sweep scanning path for complex SMH. The generated path exactly meets the unique kinematic requirements of high-speed five-axis inspection and eliminates all noninspection operations. In this way, the SMH inspection can be accomplished with high efficiency.

This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

This work proposes a scalable gamma non-negative matrix network (SGNMN), which uses a Poisson randomized Gamma factor analysis to obtain the neurons of the first layer of a network. These neurons obey Gamma distribution whose shape parameter infers the neurons of the next layer of the network and their related weights. Upsampling the connection weights follows a Dirichlet distribution. Downsampling hidden units obey Gamma distribution. This work performs up-down sampling on each layer to learn the parameters of SGNMN. Experimental results indicate that the width and depth of SGNMN are closely related, and a reasonable network structure for accurately detecting brain fatigue through functional near-infrared spectroscopy can be obtained by considering network width, depth, and parameters.

Current brain cognitive models are insufficient in handling outliers and dynamics of electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic infinite mixture model to infer the dynamics of EEG fatigue signals. The instantaneous spectrum features provided by ensemble wavelet transform and Hilbert transform are extracted to form four fatigue indicators. The covariance of log likelihood of the complete data is proposed to accurately identify similar components and dynamics of the developed mixture model. Compared with its seven peers, the proposed model shows better performance in automatically identifying a pilot's brain workload.

As one of the most significant applications of nanopositioning technology, the tremendous development of atomic force microscopes (AFMs) has been witnessed these years. Essentially, the scanning motions of AFMs are generally driven by piezoelectric tube scanners (PTSs), whose cross-coupling effect hinders their high speed and high-precision positioning. Therefore, it becomes an urgent yet challenging mission to establish a niche model for the nonlinear cross-coupling dynamics of the PTS. As an emergent pure data-driven learning approach, the Koopman operator sheds some light on the PTS modeling methodology, which is thus adopted in this article to approximate the nonlinear cross-coupling dynamics of PTSs in an infinite-dimensional space. Moreover, an online high-order extended dynamic mode decomposition algorithm is proposed for the finite-dimensional approximation of the Koopman operator online. The merit of the present model lies in updating the identified cross coupling upon the arrival of new data in an incremental way. Finally, experiments are conducted to approximate theX–Yaxes cross coupling of PTSs of an NTMDT Prima AFM, which verifies the effectiveness and superiority of the proposed modeling algorithm. This article is expected to pave the way from the Koopman operator learning theory to real applications in dynamics modeling of abundant nanoscale measurement systems.

Five-axis sweep scanning is an emerging technology, which can achieve precise and efficient contact measurement. However, existing five-axis scanning path planning algorithms either provide a feasible solution without considering measurement efficiency, or optimize the path without considering the interference-free condition. In this article, an algorithm is proposed for generating time-optimal and interference-free five-axis sweep scanning path. The measurement time is estimated by feed rate scheduling methods and used as the cost function of the optimization problem. The interference condition is determined by whether the stylus orientation is within the admissible orientation domain and formulated as the penalty function. The optimization problem is finally solved by the distributed particle swarm optimization to obtain the optimized scanning path. The experimental results validate the effectiveness of the proposed method. The proposed method has better measurement efficiency over the other existing five-axis sweep scanning path planning methods and a leading commercial software.

The force-controlled fast tool servo (FTS) is an innovative tool and demonstrated to be very promising for both the manufacturing and metrology of complex-shaped surfaces. Facing challenges in developing a force sensing FTS (FS-FTS), a novel piezo-actuated FS-FTS is developed without using external force sensors. Unlike the disturbance observer based force self-sensing, a mechanical observer is deliberately designed to provide an exact estimation of the actuation dynamics and nonlinearity. Moreover, through arranging the actuation and force sensitive end in different places in the flexure chain of the FS-FTS, the conflict between the force sensing resolution and the working bandwidth can be well resolved, providing an opportunity to simultaneously achieve a high working bandwidth and a high force sensing resolution. Assisted by an established element-node model, the dependence of the FS-FTS performance on its structural parameters is derived, and accordingly, the structure is optimized through a Pareto frontier based multiobjective optimization, which is then verified via the finite element simulation. The experiment result suggests that the obtained force sensing resolution and bandwidth are about 0.24 mN and 600 Hz, respectively. Finally, a hybrid position/force control is realized by employing a generalized impedance model.

This work designs an adversarial Bayesian deep network to solve the cognitive detection of pilot fatigue. Batch normalization and data enhancement are adopted in the posterior inference of the proposed model parameters to effectively improve the generalization of neural networks. The generator is used to enhance the brain power map generated from three cognitive indicators and improve the accuracy of fatigue state recognition. This work also adds adversarial noise in the vicinity of each brain electrode to form an adversarial image, which further reveals the correlation between the cognitive state of brain and the location of brain regions. Compared with other deep models and parameter optimization methods, our model achieves better detection accuracy.

The evaluation of pilot brain activity is very important for flight safety. This study proposes a Hidden semi-Markov Model with Hierarchical prior to detect brain activity under different flight tasks. A dynamic student mixture model is proposed to detect the outlier of emission probability of HSMM. Instantaneous spectrum features are also extracted from EEG signals. Compared with other latent variable models, the proposed model shows excellent performance for the automatic inference of brain cognitive activity of pilots. The results indicate that the consideration of hierarchical model and the emission probability with${t}$mixture model improves the recognition performance for Pilots’ fatigue cognitive level.

This article proposes a four-dimensional brain mapping method, which can represent the continuous process of a person’s fatigue state in the form of image frames in a space-time range. This work couples 3-D-convolutional neural networks and long-short-term memory networks to form a cognitive detection model of brain fatigue dynamics, which can simulate the continuous process of a person’s brain fatigue dynamics and accurately identify different cognitive fatigue states. Our approach can be applied to any type of brain fatigue detection.

Odd-harmonic repetitive control (ORC) has been successfully applied to improve the triangular trajectory tracking performance of nanopositioners. However, the conventional ORC tends to amplify the tracking errors at frequencies other than the odd-harmonic components, mainly the even harmonics of the fundamentals of the intended triangular trajectory to be tracked. Due to the influence from the hysteresis nonlinearity of the piezoelectric actuator, this would result in significant tracking errors. To overcome this limitation, this article proposes an enhanced odd-harmonic repetitive control (EORC) using the spectrum-selection filtering scheme to improve the loop-shaping property of the ORC. This effectively eliminates the problem of amplifying the tracking errors while preserving the advantages of the conventional ORC, such as fast convergence speed and low computation cost. The EORC is combined with a proportional–integral tracking controller to improve the tracking performance. The controller design, stability analysis, and performance evaluation are presented. The experimental results demonstrating the effectiveness of the proposed EORC-based control scheme are presented showing the excellent tracking of triangular trajectories with fundamental frequencies up to 1000 Hz. Moreover, a reduction in rms tracking errors by up to 52% is achieved.Note to Practitioners—The high-speed atomic force microscopy (AFM) plays an increasingly vital role in observing and manipulating objects at the nanoscale. In the raster scanning of AFMs, the most challenging issue is the triangular trajectory tracking of nanopositioning stages with high precision. Although the odd-harmonic repetitive control (ORC) has been successfully applied to improve the tracking performance, the conventional ORC would amplify the tracking errors distributed at the frequencies other than the odd harmonics, especially those at the even harmonics, resulting from the inherent complicated hysteresis nonlinearity. This problem of amplifying the tracking errors would lead to larger tracking errors, deteriorating the performance of AFM imaging. To address this issue, this article proposes an enhanced ORC using the spectrum-selection filtering scheme to improve the loop-shaping property of the ORC, so as to eliminate the problem of amplifying the tracking errors while preserving the advantages of the conventional ORC, such as fast convergence speed and low computation cost. The experimental results show that, with this simple modification, the positioning errors are reduced greatly, all-the-while preserving the benefits of the conventional ORC scheme– fast convergence speed and low computation cost. In terms of tracking accuracy and the simple structure, this development can be easily implemented to other systems that challenge from the tracking accuracy under ORC scheme.

The resonant controller (RC), as a promising candidate for high-speed nonraster nanopositioning applications, can track the sinusoidal reference with zero steady-state error. This article presents a controller composed of several RCs in parallel for tracking nonraster sequential scanning trajectories. The selection for each RC in the parallel array is based on two considerations: one is the spectrum of the reference signal and the other is the harmonics caused by the nonlinearities of the nanopositioning stage. The performance of RC is highly dependent on the accurate placement of the resonant poles, but unfortunately, many existing digital implementation methods could cause a deviation of the resonant poles from their initial locations. To address this problem, a modified Tustin (MTus) method is proposed in this article to implement the controller with better accuracy. Furthermore, the fractional-order (FO) calculus is introduced to improve the transient performance of the RCs. To validate the proposed methods, a comprehensive examination of several types of the nonraster sequential scanning trajectories with a wide frequency range has been carried out on a nanopositioning stage. The results have been compared with other methods, showing that the tracking errors are reduced significantly under the controller implemented by the MTus method especially in high-frequency conditions and that the application of the FO calculus reduces the settling time of the controller by more than 30% in most cases.Note to Practitioners—The demand for high-speed atomic force microscope (AFM) increases rapidly. However, the commonly used raster trajectory limits the achievable scan speed of the AFM. An effective way to improve the scanning and imaging speed of the AFM is the application of the sequential nonraster scanning methods. The trajectories of sequential nonraster scanning patterns mainly composed of few sinusoid signals with different frequencies. Therefore, the resonant controller (RC) is introduced in this article as it is capable to track the sinusoidal reference with zero steady-state error. Several RCs are selected first based on the spectrum analysis of the reference and the consideration of the harmonics caused by the system nonlinearities, and then, they are connected in parallel to form the controller for precise tracking of the reference. In order to realize the digital implementation of the designed controller, a modified Tustin discretization method is proposed, which ensures the accurate resonant pole placement of the RC and thus maintains the tracking performance of the RC. In addition, the fractional-order (FO) calculus is introduced to speed up the convergence of the designed controller while preserving the tracking accuracy, and this parallel-structure FO RC (PSFORC) design can be implemented to other systems that require high-speed and high-accuracy tracking of the periodic signals.

Pilots' brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteration, and then the hidden units of the gamma distribution are downsampled. An effective upper and lower Gibbs sampler is formed to realize the automatic reasoning of the network structure. In order to extract the 3-D instantaneous time-frequency distribution spectrum of electroencephalogram (EEG) signals and avoid signal modal aliasing, this article also proposes a smoothed pseudo affine Wigner-Ville distribution method. Finally, experimental results show that our model achieves satisfactory results in terms of both recognition accuracy and stability.

How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed. In order to obtain the salient features of the spherical signal, a rotating spherical Haar wavelet is also proposed, which has a consistent scale in the same direction between the reconstructed image and the original image. Second, in order to find a better characteristic representation of the spherical signal, a higher contractive autoencoder (HCAE) is designed for the potential representation of the spherical Haar wavelet coefficients, which has two penalty items, respectively, from Jacobian and two order items from Taylor expansion of the point x for the contract learning of sample space. Third, in order to improve the classification performance, this paper proposes a fuzzy Gaussian support vector machine (FGSVM) as the top classification tool of the deep learning model, which can punish some Gaussian noise from the output of the deep HCAE network (DHCAEN). Finally, a DHCAEN-FGSVM classifier is proposed to identify the location of the pupil center. The experimental results of the public data set and actual data show that our model is an effective method for spherical signal detection.

Workload assessment faces two major issues. That is, how to learn effective fatigue characteristics and how to find the potential state of the workload. This paper proposes a solution to assess the brain fatigue workload of pilots through an instantaneous spectral entropy feature and an infinitely warped model. The instantaneous characteristics of electroencephalography (EEG) signals are extracted by Hilbert transform, and Euclidean norm weighted permutation entropy is proposed. The infinitely warped model is a new automatic learning model for detecting arbitrary shapes of EEG data. In addition, we propose a rapid learning framework to learn mental fatigue by integrating Treelet transform and infinitely warped models. Compared to other state-of-the-art methods, our approach is better able to handle complex data in complex shapes. The experimental results show that this method can more effectively assess the brain fatigue of pilots.

Large skin parts play an important role in the aerospace industry. The wall thickness of the machined pocket in the skin part needs to be strictly controlled to ensure the transport capacity and structural strength. The wall-thickness accuracy is generally decreased by various factors, such as the shaping error of the workpiece blank, fixing error, machine tool error, and deformation caused by cutting force or internal stress. These factors are usually inevitable and stochastic due to the extremely weak rigidity and easy-to-deflect characteristics of the large skin parts. To ensure the wall-thickness accuracy, a fuzzy v-support vector machine (Fv-SVM)-based wall-thickness error decomposition method is proposed. The wall-thickness errors, which are monitored in the cutting process, are decomposed into spatial-related errors and time-related errors. The Fv-SVM-based decomposition method with the principle of spatial statistical analysis is a data-driven approach for intelligent manufacturing. The data-driven method can consider all factors that affect the wall-thickness accuracy, while the model-driven method usually only considers one factor, such as the workpiece deformation or fixing error. After decomposition, the spatial-related wall-thickness error is offline compensated, and the time-related wall-thickness error is compensated by using a real-time strategy. The novel method can be applied to complex tool paths. The cutting experiment of rectangular pockets in a large skin panel was conducted to verify the effectiveness of the proposed method. The wall-thickness accuracy can be improved to 0.05 mm for the workpiece with only 2 mm thickness.

In this paper, a modified repetitive control (MRC) based approach is developed for high-speed tracking of nanopositioning stages. First, the hysteresis nonlinearity is decomposed as a periodic disturbance over a linear system. Then, the MRC technique is utilized to account for the periodic disturbances/errors caused by the hysteresis and dynamics behaviors. The developed approach provides a simple and effective hysteresis compensation strategy, avoiding the constructions of hysteresis model and its inversion. Besides, with improved loop-shaping properties, the MRC can alleviate the nonperiodic disturbance amplification problem of the conventional repetitive control. Finally, the effectiveness and performance of the developed MRC-based approach are verified by the experimental results on a custom-built piezo-actuated stage in terms of hysteresis compensation, disturbance rejection and tracking accuracy.

Soft dielectric elastomer actuators (DEAs) exhibit interesting muscle-like behavior for the development of soft robots. However, it is challenging to model these soft actuators due to their material nonlinearity, nonlinear electromechanical coupling, and time-dependent viscoelastic behavior. Most recent studies on DEAs focus on issues of mechanics, physics, and material science, while much less importance is given to quantitative characterization of DEAs. In this paper, we present a detailed experimental investigation probing the voltage-induced electromechanical response of a soft DEA that is subjected to cyclic loading and propose a general constitutive modeling approach to characterize the time-dependent response, based on the principles of nonequilibrium thermodynamics. In this paper, some of the key observations are found as follows: 1) Creep exhibits the drift phenomenon, and is dominant during the first three cycles. The creep decreases over time and becomes less dominant after the first few cycles; 2) a significant amount of hysteresis is observed during all cycles and it becomes repeatable after the first few cycles; 3) the peak of the displacement is shifted from the peak of the voltage signal and occurs after it. To account for these viscoelastic phenomena, a constitutive model is developed by employing several dissipative nonequilibrium mechanisms. The quantitative comparisons of the experimental and simulation results demonstrate the effectiveness of the developed model. This modeling approach can be useful for control of a viscoelastic DEA and paves the way to emerging applications of soft robots.

Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies.

This paper presents a novel high-bandwidth control approach for piezo-actuated nanopositioning stages. A delayed position feedback (DPF) controller is first developed in the inner loop to damp the resonant mode of piezo-actuated stages. A generalized Runge-Kutta method (GRKM) is proposed to determine the parameters of the DPF controller with pole placement. The benefit of the DPF for active damping is its simple structure and ease of implementation. Then, a high-gain proportional-integral (PI) controller is designed in the outer loop to deal with the hysteresis nonlinearity, disturbance and modeling errors. The stability of the control system is analyzed via a graphical method. Finally, experiments are conducted to demonstrate the effectiveness and superiority of the proposed approach in terms of tracking accuracy at high speed as compared to the PI controller.

Fixtures are used in almost all modern manufacturing operations. In practice, there is a wide range of specifications on the manufacturing precision and thus different requirements on workpiece locating accuracy during each production process. In view of that, this paper developed a unified signed distance function framework. Under this framework, three systems of sensitivity equations, which link the locator source errors to the resulting workpiece localization error, were derived. Accordingly, unique linear, one-sided quadratic, and two-sided quadratic models have been developed. These three models, distinguished by whether or not taking into account workpiece and/or locator curvature effects, provide a range of locating precision analysis which is illustrated and verified by several examples. The developed modeling technique can handle general fixture locating rather than being limited to certain locating schemes. The proposed models are of practical relevance and have great potential to be applied towards locating scheme evaluation, fixture design, fault diagnosis, and tolerance analysis.

The aim of this paper is to develop an integral equation based spectral method for prediction of chatter stability in low radial immersion milling. First, the delay-differential equation with time-periodic coefficients governing the dynamic milling process is transformed into the integral equation. Then, the duration of one tooth period is divided into the free vibration and the forced vibration processes. While the former one has an analytical solution, the discretization technique is explored to approximate the solution of the latter one. After the forced vibration duration being equally discretized, the Gauss-Legendre formula is used to discretize the definite integral, in the meantime the Lagrange interpolation is adopted for approximating the state item and the time-delay item by using the corresponding discretized state points and time-delay state points. The approximate Floquet transition matrix is thereafter constructed to predict the milling stability based on the Floquet theory. The benchmark examples are utilized to verify the proposed method. Compared with previous time domain methods, the proposed method enables higher rate of convergence. The results also demonstrate that the proposed method is high-effective.

In this paper, a new type of decoupled 2-DOF translational parallel micro-positioning stage is designed to realize the 2-DOF ultra-precision linear motion. The stage consists of two piezoelectric actuators (PZTs) and a monolithic compliant mechanism. The monolithic compliant mechanism adopts two types of compound double parallel four-leaf flexures and a mirror symmetric structure to reduce the input and output cross coupling and parasitic motion. Based on the stiffness matrix method and screw theory, a mathematical model is constructed to analyze the compliant mechanism. The optimal design is performed in view of performance constraints. The design results show good static and dynamic performances of the stage, which are well validated by the simulation of finite-element-analysis (FEA) and experimental results. The experimental results show that the proposed stage has a full range of 40µm × 40µm when the full voltage(100V) is applied on the two PZTs. Besides, the stage only has the maximum cross coupling of -50dB between the two axes, low enough to utilize single-input-single-out(SISO) control strategies for positioning and tracking.

This paper addresses the problem of measurement point planning for 3-D workpiece localization in the presence of part surface errors and measurement errors. A number of frame-invariant norms of the infinitesimal rigid body displacement are defined to quantify the localization accuracy required by manufacturing processes. Then, two kinds of frame-invariant indices are derived to characterize the sensitivities of the accuracy measures to the sampling errors at the measurement points. With a dense set of discrete points on the workpiece datum surfaces pre-defined as candidates for measurement, planning of probing points for accurate recovery of part location is modeled as a combinatorial problem focusing on minimizing the accuracy sensitivity index. A heuristic floating forward search algorithm is presented to efficiently find a near-optimal solution. An example confirms the validity of the presented criteria and algorithm.