Publications

High-Speed Tracking of a Nanopositioning Stage Using Modified Repetitive Control

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.

Robust tracking of nanopositioning stages using sliding mode control with a PID sliding surface

This paper presents a novel sliding mode control scheme for robust tracking control of a nanopositioning stage composed of piezoceramic stack actuators (PSAs) and compliant flexure mechanisms. The developed controller is distinguished by using a proportional-integral-derivative (PID) type sliding surface rather than the traditional proportionalderivative (PD) type sliding surface, which is more effective to reduce the tracking error. The stability of the developed control law is proved by Lyapnuov analysis. Finally, comparative studies are performed on a custom-built PSA-actuated stage to demonstrate the effectiveness of the developed controller. The major advantages of the developed control scheme lie in that: i) both the nominal system model and hysteresis model are not required in the control law; ii) it is ease of real-time implementation just like the PID control but with the improved tracking performance.

Odd-harmonic repetitive control for high-speed raster scanning of piezo-actuated nanopositioning stages with hysteresis nonlinearity

In raster scanning applications of atomic force microscopies, precisely tracking periodic triangular trajectories is the major objective of nanopositioning stages. Considering the fact of periodic operations, the repetitive control technique becomes promising and has been recently developed to reduce tracking errors. In our new experiments, it is found that, with triangular reference input, the hysteresis nonlinearity mainly affects the system at the odd harmonics of the input signal. In this sense, an odd-harmonic repetitive control (ORC) strategy is proposed to handle the hysteresis nonlinearity, with the hysteresis treated as the odd-harmonic periodic disturbance. Therefore, it avoids the modeling and inverting of the complex hysteresis nonlinearity. Another benefit of the developed ORC strategy is that it can also account for the tracking errors caused by the linear dynamics effect. To verify the effectiveness of the ORC strategy, real-time experiments are performed on a custom-built piezo-actuated nanopositioning stage. Experimental results show that the developed ORC strategy achieves precise tracking of 1562.5-Hz triangular trajectory with the hysteresis nonlinearity mitigated to a negligible level, which demonstrates the feasibility and effectiveness of the developed ORC strategy on hysteresis compensation during high-speed raster scanning.

Lightweight soft neuroprosthetic hand

Mainly composed of electrical motors and sophisticated mechanical components, existing neuroprosthetic hands1,2 are typically heavy (>400 g) and expensive (>USD 10,000), and they lack the compliance and tactile feedback of human hands. These limitations hamper neuroprosthetic hands’ innovation and broad utility for amputees3-5. Here we report the design, fabrication and applications of a lightweight (292 g) and potentially low-cost (component cost below USD 500) soft neuroprosthetic hand with simultaneous myoelectric control and tactile feedback. The soft neuroprosthetic hand consists of five soft fingers and a palm to give six active degrees of freedom under pneumatic actuation, four electromyography sensors that measure the surface electromyogram signals to control the hand to deliver four common grasp types, and five hydrogel-elastomer capacitive sensors on the fingertips that measure the touch pressure and elicit electrical stimulation on the skin of the residual limb. The soft finger is made of a fiber-reinforced elastomeric structure embedded with rigid segments to mimic the soft-joint/rigid-bone anatomy of the human finger. We use a set of standardized tests6 to compare the speed and dexterity of the soft neuroprosthetic hand and a conventional rigid neuroprosthetic hand7 on two transradial amputees. The soft neuroprosthetic hand gives overall superior performances to the rigid hand. We further demonstrate that one transradial amputee wearing the soft neuroprosthetic hand can regain the versatile hand functions with primitive touch sensation and real-time closed-loop control in daily activities such as handling tools, eating, shaking hands, petting animals, and recognizing touch pressure. This work not only represents a new paradigm for designing soft neuroprosthetic devices but also opens an avenue to widespread applications of lightweight, low-cost, and compliant hand replacements for amputees.

High-bandwidth tracking control of piezo-actuated nanopositioning stages using closed-loop input shaper

An integrated control strategy for piezo-actuated nanopositioning stages is proposed in this paper. The aim is to achieve high-speed and high-precision tracking control of nanopositioning stages. For this purpose, a direct inverse compensation method is firstly applied to eliminate the hysteresis nonlinearity without involving inverse model calculation. Then, an inside-the-loop input shaper is designed to suppress the vibration of the compensated system. A Smith predictor is introduced to prevent the potential closed-loop instability caused by the time delay of the inside-the-loop input shaper. Finally, a high-gain feedback controller is employed to handle the disturbances and modeling errors. To demonstrate the effectiveness of the proposed control method, comparative experiments are carried out on a piezoelectric actuated stage. The results show that the proposed control approach increases the tracking bandwidth of the stage from 22.6Hz to 510Hz.

Design and Application of High-Stretchability and Variable-Tensile-Stiffness Fiber via Crossing Layer Jamming Structure

Soft human–machine interfaces: design, sensing and stimulation

No abstract is provided for this article.

A Pneumatic Soft Gripper with Configurable Workspace and Self-sensing

In this paper, we present a novel pneumatic soft gripper with a configurable workspace and perception to grasp various objects and recognize their sizes. The soft gripper consists of three pneu-net soft fingers embedded with resistive strain sensors and cascaded by a stretchable palm. The pneu-net soft fingers are fabricated through a lost-wax casting process. Each strain sensor is designed with an ionic hydrogel-elastomer hybrid structure and embedded into a soft finger to recognize its deformation. The stretchable palm is designed with an opening-closing parallel mechanism driven by a pneumatic fiber-reinforced soft actuator to modify the grasping workspace of the gripper. The characterization experiments are conducted to demonstrate the excellent performance of soft gripper. Based on the measurement of the strain sensors, we propose two kinds of grasping strategies for the soft gripper: a traditional finger-bending identification strategy (FBI strategy) without the active palm and a new palm-closing identification strategy (PCI strategy) with the active palm. Experimental results with an industrial robot demonstrate that our soft gripper with the PCI strategy can perform more robust picking tasks and more accurate identification tasks than with the FBI strategy.

Precise Robotic Needle-Threading with Tactile Perception and Reinforcement Learning

This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: Tail-end Finding and Tail-end Insertion. In the first stage, the agent traces the contour of the thread twice using vision-based tactile sensors mounted on the gripper fingers. The two-run tracing is to locate the tail-end of the thread. In the second stage, it employs a tactile-guided reinforcement learning (RL) model to drive the robot to insert the thread into the target needle eyelet. The RL model is trained in a Unity-based simulated environment. The simulation environment supports tactile rendering which can produce realistic tactile images and thread modeling. During insertion, the position of the poke point and the center of the eyelet are obtained through a pre-trained segmentation model, Grounded-SAM, which predicts the masks for both the needle eye and thread imprints. These positions are then fed into the reinforcement learning model, aiding in a smoother transition to real-world applications. Extensive experiments on real robots are conducted to demonstrate the efficacy of our method. More experiments and videos can be found in the supplementary materials and on the website: https://sites.google.com/view/tac-needlethreading.

Kinematic Modeling and Characterization of Soft Parallel Robots

Parallel robots with rigid transmission mechanisms have been widely developed to improve the speed, precision, and load capability. However, it is still challenging in promoting soft parallel robots due to the difficulty in accurate kinematic modeling of soft continuum links. In this article, we present a general framework on the design, kinematic modeling, model-based characterization, and control for a class of soft parallel robots. The designed soft parallel robot consists of three fiber-reinforced soft pneumatic actuators, a base stage, and an output stage. With the introduction of the mathematical toolkit of the absolute nodal coordinate formulation, we develop a continuum-based model to describe and parameterize both the global complex configuration and the local large deformation of the soft parallel robot. In this sense, the mappings among the defined kinematic spaces of the robot can be characterized through force analysis. Based on the developed model, we next analyze the robot's workspace and stiffness with different design parameters, which are also verified by a set of experiments. Finally, we establish a model-based trajectory tracking controller for the soft parallel robot. The experimental results demonstrate that with the feedforward controller, the end effector of the soft parallel robot can well follow the desired trajectories under different output velocities, where the average positioning error is about 2.6–3.9% of the maximum length of the workspace.

Multimaterial cryogenic printing of three-dimensional soft hydrogel machines

Hydrogel-based soft machines are promising in diverse applications, such as biomedical electronics and soft robotics. However, current fabrication techniques generally struggle to construct multimaterial three-dimensional hydrogel architectures for soft machines and robots, owing to the inherent hydrogel softness from the low-density polymer network nature. Herein, we present a multimaterial cryogenic printing (MCP) technique that can fabricate sophisticated soft hydrogel machines with accurate yet complex architectures and robust multimaterial interfaces. Our MCP technique harnesses a universal all-in-cryogenic solvent phase transition strategy, involving instant ink solidification followed by in-situ synchronous solvent melting and cross-linking. We, therefore, can facilely fabricate various multimaterial 3D hydrogel structures with high aspect ratio complex geometries (overhanging, thin-walled, and hollow) in high fidelity. Using this approach, we design and manufacture all-printed all-hydrogel soft machines with versatile functions, such as self-sensing biomimetic heart valves with leaflet-status perception and untethered multimode turbine robots capable of in-tube blockage removal and transportation. Hydrogel-based machines have potential in a range of applications, but it can be challenging to prepare multimaterial devices with complex structures. Here, the authors report the development of a multimaterial cryogenic printing technique that can be used to prepare three-dimensional structures with geometries such as overhanging, thin-walled and hollow.

Deep transfer learning-based adaptive gesture recognition of a soft e-skin patch with reduced training data and time

Deep learning-based classification algorithms are promising in gesture recognition with soft e-skin patches. However, the reported algorithms usually require large amount of training data, resulting in the time-consuming data collection process. In this paper, we present a deep transfer learning-based adaptive strategy for accurate gesture recognition of a soft e-skin patch with reduced training data and time. To this end, we first train a base neural network as the general feature extraction network. Next, we transfer the front layers of the pre-trained base network to target networks of new gesture recognition tasks. Further, we apply the fine-tune technique to refine the copied parameters. Finally, with our custom-built soft e-skin patch, we experimentally verify the developed strategy on two typical transfer cases, termed as the user transfer case (Case I) and the gesture transfer case (Case II). The experimental results show that, to ensure the stable accuracy of 95 %, the training data with and without the adaptive strategy are 1,312 vs 10,912 for Case I, and 8,192 vs 12,032 for Case II, respectively. In this sense, the training time of target networks can be reduced by 62.96 % for Case I and 34.20 % for Case II, respectively. This work shows the potential to promote the widespread application of e-skins in human computer interaction.

Modeling and compensating the dynamic hysteresis of piezoelectric actuators via a modified rate-dependent Prandtl–Ishlinskii model

This paper presents a modified rate-dependent Prandtl–Ishlinskii (MRPI) model for the description and compensation of the rate-dependent asymmetric hysteresis in piezoelectric actuators. Different from the commonly used approach with dynamic weights or dynamic thresholds, the MRPI model is formulated by employing dynamic envelope functions into the play operators, while the weights and thresholds of the play operators are still static. By this way, the developed MRPI model has a relatively simple mathematic format with fewer parameters and easier parameter identification process. The benefit for the developed MRPI model also lies in the fact that the existing control approaches can be directly adopted with the MRPI model for hysteresis compensation in real-time applications. To validate the proposed model, an open-loop tracking controller and a closed-loop tracking controller are developed based on a dynamic hysteresis compensator, which is directly constructed with the MRPI model. Comparative experiments are carried out on a piezo-actuated nanopositioning stage. The experimental results demonstrate the effectiveness and superiority of the controllers based on the developed MRPI model compared to the controllers based on the rate-independent P–I model and the rate-dependent P–I model with dynamic weighting functions.

Robust Mechanically Interlocked Network Ionogels

Ionogels have attracted considerable attention as versatile materials due to their unique ionic conductivity and thermal stability. However, relatively weak mechanical performance of many existing ionogels has hindered their broader application. Herein, we develop robust, tough, and impact‐resistant mechanically interlocked network ionogels (IGMINs) by incorporating ion liquids with mechanical bonds that can dissipate energy while maintain structural stability. Profiting from the dynamic yet stable nature of the mechanically interlocked networks, IGMINs exhibit high tensile strength (9.6 MPa), fracture energy (39 kJ/m2), and toughness (25.9 MJ/m3), along with a high elongation rate (473%) and excellent impact resistance and shape memory, resulting in overall performance that surpasses most reported ionogels. Furthermore, in the application of strain sensors for monitoring the gait of crawling robots, the toughness and robustness of IGMINs ensure their ability to consistently output stable electrical signals during the stretching and contraction processes, thereby highlighting their practical application potential. Our work provides a new research strategy for toughening ionogels and promotes the development of mechanically interlocked materials.

Bio-inspired annelid robot: a dielectric elastomer actuated soft robot

Biologically inspired robots with inherent softness and body compliance increasingly attract attention in the field of robotics. Aimed at solving existing problems with soft robots, regarding actuation technology and biological principles, this paper presents a soft bio-inspired annelid robot driven by dielectric elastomer actuators (DEAs) that can advance on flat rigid surfaces. The DEA, a kind of soft functional actuator, is designed and fabricated to mimic the axial elongation and differential friction of a single annelid body segment. Several (at least three) DEAs are connected together into a movable multi-segment robot. Bristles are attached at the bottom of some DEAs to achieve differential friction for imitating the setae of annelids. The annelid robot is controlled by periodic square waves, propagating from the posterior to the anterior, which imitate the peristaltic waves of annelids. Controlled by these waves, each DEA, one-by-one from tail to head, anchors to the ground by circumferential distention and pushes the front DEAs forward by axial elongation, enabling the robot to advance. Preliminary tests demonstrate that a 3-segment robot can reach an average speed of 5.3 mm s-1 (1.871 body lengths min-1) on flat rigid surfaces and can functionally mimic the locomotion of annelids. Compared to the existing robots that imitate terrestrial annelids our annelid robot shows advantages in terms of speed and bionics.

Fractional repetitive control of nanopositioning stages for tracking high-frequency periodic inputs with nonsynchronized sampling

The repetitive control (RC) has been employed for high-speed tracking control of nanopositioning stages due to its abilities of precisely tracking periodic trajectories and rejecting periodic disturbances. However, in digital implementation, the sampling frequency should be integer multiple of the tracking frequency of the desired periodic trajectory. Otherwise, the rounding error would result in a significant degradation of the tracking performance, especially for the case of high input frequencies. To mitigate this rounding effect, the fractional repetitive control (FRC) technique is introduced to control the nanopositioning stage so as to precisely track high-frequency periodic inputs without imposing constraints on the sampling frequency of the digital control system. The complete procedure of controller design and implementation is presented. The techniques to deal with the problems of non-minimum phase system and fractional delay points number are described in detail. The proposed FRC is plugged into the proportional-integral control, and implemented on a custom-built piezo-actuated nanopositioning stage. Validation experiments are conducted, and the results show that the tracking errors caused by the rounding effect in the traditional RC approach are almost completely eliminated, when tracking sinusoidal waveforms with frequencies from 1000 Hz to 1587.3 Hz under the sampling frequency of 50 kHz, where the fractional parts being rounded vary from 0 to 0.5.

A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback

No abstract is provided for this article.

X-crossing pneumatic artificial muscles

Artificial muscles are promising in soft exoskeletons, locomotion robots, and operation machines. However, their performance in contraction ratio, output force, and dynamic response is often imbalanced and limited by materials, structures, or actuation principles. We present lightweight, high-contraction ratio, high-output force, and positive pressure-driven X-crossing pneumatic artificial muscles (X-PAMs). Unlike PAMs, our X-PAMs harness the X-crossing mechanism to directly convert linear motion along the actuator axis, achieving an unprecedented 92.9% contraction ratio and an output force of 207.9 Newtons per kilogram per kilopascal with excellent dynamic properties, such as strain rate (1603.0% per second), specific power (5.7 kilowatts per kilogram), and work density (842.9 kilojoules per meter cubed). These properties can overcome the slow actuation of conventional PAMs, providing robotic elbow, jumping robot, and lightweight gripper with fast, powerful performance. The robust design of X-PAMs withstands extreme environments, including high-temperature, underwater, and long-duration actuation, while being scalable to parallel, asymmetric, and ring-shaped configurations for potential applications.