Accurate robotic external force estimation is fundamental to sensorless physical human-robot interaction (pHRI), as it enables robots to interact with environments compliantly and safely. While the Kalman filter-based generalized momentum force estimation method (KF-GM) is widely adopted, its static covariance matrices and Gaussian noise assumption constrain adaptability and robustness, degrading estimation accuracy. This paper proposes a novel Hybrid Adaptive-Robust Kalman Filtering Approach (HARKF) integrating adaptive Kalman filter (AKF) and robust Kalman filter (RKF), with real-time covariance adjustment and outlier rejection, substantially improving adaptability and robustness. However, the fusing of AKF and RKF introduces inherent inter-filter coupling interferences, significantly compromising estimation accuracy due to incompatible noise adaptation mechanisms. Therefore, a noise-type-based module decoupling scheme and a parameter transfer mechanism are proposed, establishing synergistic collaboration between AKF and RKF, where their complementary mechanisms enable reciprocal reinforcement. The decoupling scheme eliminates cross-coupling through noise characteristic analysis, thus preserving system adaptability while enhancing disturbance robustness, resulting in significantly enhanced force estimation accuracy. The transfer mechanism resolves inter-filter parameter conflicts, thereby considerably improving filtering continuity and estimation robustness. Experimental results indicate that compared with existing Kalman filter-based methods, HARKF exhibits superior force estimation accuracy across diverse interactive scenarios.

Visual servoing is a critical technique in robotic applications, enabling robots to accurately reach target positions using visual information. Traditional methods, while offering high precision, often suffer from limited convergence domain and strong reliance on prior knowledge. In contrast, learning-based approaches alleviate these limitations but typically face challenges in generalization due to limited training data. Recent studies have demonstrated that leveraging large-scale synthetically generated data in simulation can significantly improve the generalization ability of learning-based models. Inspired by these works, this paper presents an end-to-end pipeline for data generation, model training, and simulation deployment based on Isaac Sim. Leveraging its high-fidelity visual simulation capabilities, the proposed framework enables large-scale dataset generation and algorithm validation for visual servoing tasks. Experimental results demonstrate that models trained with the generated large-scale datasets exhibit improved generalization (over 13% increase of success rate) and faster convergence (approximately 2.5× faster) under specific supervision settings. This paper confirms the effectiveness of large-scale data in enhancing model performance and offers a complete and efficient solution for algorithm development and evaluation in visual servoing.

This paper proposes an adaptive output feedback trajectory tracking control scheme for permanent magnet linear synchronous motors (PMLSMs). An integral sliding mode observer (ISMO) is developed to simultaneously estimate lumped disturbances and velocity state without requiring disturbance differentiability and velocity measurement. A command filter backstepping control (CFBC) strategy replaces derivative operations in conventional backstepping designs, with a compensation mechanism introduced for filter errors, simplifying the controller design and improving tracking performance. In addition, an adaptive law is designed to update model parameters online, enhancing robustness and adaptability. Experiments are conducted on an iron-core PMLSM platform and results show that the proposed method achieves superior tracking performance.

Significant differences in spatial structure characteristics make the coarse-grained few-shot scenarios easier to handle. However, fine-grained scenes remain often more challenging, and current related research falls far short of human-level recognition capabilities. To address this tough challenge, we get inspiration by the structures and functions of the human visual system, and then propose a human-centric visual cognition recognition framework, named VCRNet, including a shallow cognition network VCRNet-4 and the deep cognition neural network VCRNet-12. Specifically, in this framework, we cleverly design a visual perception module to simulate powerful visual pathway feature encoding of human visual system, an attentional regional sensing module and another pixel-level sensing module to simulate the structural and shape-detailed perception capabilities of the human occipital lobe, and a cognitive recognition module to emulate the integrative cognitive abilities of human frontal lobe. Ulteriorly, we have evaluated the performances of our proposed methods on three publicly available fine-grained benchmarks: CUB-200-2011, Aircraft-Fewshot, and Stanford-Cars. The compelling comparative experiments and thoroughly justified validation study demonstrate the structural excellence and superior performance of our VCRNet framework. To facilitate subsequent research, our codes have been available.

Meta-metric learning has demonstrated strong performance in coarse-grained few-shot situations. However, despite their simplicity and availability, these metametrics are limited in effectively handling fine-grained few-shot scenarios. Fine-Grained Few-Shot Classification (FGFSC) presents significant challenges to the network's ability to extract subtle features. Equipped with the symmetrical binocular perception system and complex neural networks in the brain, humans inherently possess exceptional and resilient meta-learning abilities, facilitating superior management of fine-grained few-shot scenarios. In this paper, inspired by the human binocular visual system, we pioneer the first human-like meta-metric paradigm: Binocular Singular Hellinger Metametric (BinoHeM). Functionally, BinoHeM incorporates advanced symmetric binocular feature encoding and recognition mechanisms. Structurally, it integrates two binocular sensing feature encoders, a singular Hellinger metametric, and two collaborative identification mechanisms. Building on this foundation, we introduce two innovative metametric variants: BinoHeM-KDL and BinoHeM-MTL. These are grounded in two advanced training mechanisms: knowledge distillation learning (KDL) and meta-transfer learning (MTL), respectively. Furthermore, we showcase the high accuracy and robust generalization capabilities of our approaches on four representative FGFSC benchmarks. Extensive comparative and ablation experiments have validated the efficiency and superiority of our paradigm over other state-of-the-art algorithms. Our code is publicly available at: https://github.com/ChaofeiQI/BinoHeM.

In this article, a velocity-free adaptive controller is proposed for the tracking control of servo mechanisms with friction compensation. A continuously differentiable friction model is employed to compensate for the dominant friction nonlinearity of servo mechanisms. Besides, a projection-type adaptive law is applied to handle parameter uncertainties in the system model. Since only the output position signal is directly measurable, an adaptive extended state observer (AESO) is constructed to estimate the indeterminate velocity state, which can also provide an estimation of unmodeled dynamics. Moreover, the dynamic gain switching of AESO can effectively suppress the peaking phenomenon at the motion beginning. Specifically, the parameter adaptation law utilizes the desired velocity state instead of the estimated value, avoiding the coupling problem between parameter and state estimation. The proposed control strategy theoretically demonstrates the transient performance and boundedness of the error in output tracking. Asymptotic stabilization of the system can also be implemented when only parameter uncertainty exists. Comparative experiments are conducted on a linear motor platform to demonstrate the effectiveness of the proposed control scheme.

We have implemented a practical and high-performance non-removable and non-adjustable online 3D box packing algorithm. The problem to be solved by the algorithm belongs to a type of online 3D box packing problem (3D-BPP), but unlike the traditional 3D box packing problem, only a limited number of boxes to be loaded can be known at a time, so the size of boxed is random for algorithm. The problem also requires that the boxes can't be placed in the buffer or the state of the already loaded boxes can't be changed during the whole process. Due to realistic factors, the packing strategy must also satisfy geometric, stability and orientation constraints. We propose a reward function based on spatial residual value assisting the best deep reinforcement learning algorithm we know right now to solve such a question. The residual value of space means the value of the space that can be used in the future. The algorithm adjusts the network parameters in the Actor-Critic framework based on the impact of the intelligence's strategy on the spatial residual value. Compared with recent online 3D box packing strategies, our algorithm performs better than the best algorithm we know with normal reward function (of course better than all current heuristic methods), better than those learning-based methods (about 9% for space utilization), and have fewer learning iterations to converge (about 1000 in all 8000 episodes).

Style transfer techniques have found wide-ranging applications in diverse domains, including image enhancement, film and animation production, augmented reality, and social media, garnering significant attention across various disciplines. However, prevailing approaches mainly rely on data-driven and adaptive normalization techniques for learning transformation matrices, while overlooking critical aspects such as multi-scale feature extraction, local-global distributions, and spatial-channel dimensions information. In this paper, we present a novel Adaptive Information Fusion Network (AIFN), comprising an encoder, an information fusion module, and a decoder symmetric to the encoder. Specifically, the information fusion module receives multi-scale feature maps extracted from a pre-trained encoder, consisting of three parallel sub-modules: Adaptive Attention Normalization (AdaAttN), Spatial-channel correlation, and a Linear submodule. Through adaptive learning of sub-module weights, we seamlessly integrate style features to achieve a harmonious fusion. Furthermore, we introduce illumination loss, ink wash loss, and identity loss to enhance stylization performance concerning lighting variations, global hue and diffusion mode, while retaining accurate and rich content features. Through comparison and ablation experiments, the proposed method produces high-quality stylized images, demonstrating excellent performance in arbitrary style transfer tasks.

Compared with traditional rigid objects' dynamic throwing and catching by the robot, the in-flight trajectory of nonrigid objects (incredibly variable centroid objects) throwing is more challenging to predict and track. This article proposes a variable centroid trajectory tracking network (VCTTN) with the fusion of vision and force information by introducing force data of throw processing to the vision neural network. The VCTTN-based model-free robot control system is developed to perform highly precise prediction and tracking with a part of the in-flight vision. The flight trajectories dataset of variable centroid objects generated by the robot arm is collected to train VCTTN. The experimental results show that trajectory prediction and tracking with the vision-force VCTTN is superior to the ones with the traditional vision perception and has an excellent tracking performance.

In this article, a hybrid visual-ranging servoing method is proposed to realize high-precision positioning tasks with a 6-degree of freedom (DOF) manipulator. This method utilizes the image and measurement features directly in the control loop. Without the need of complex image feature design and attitude estimation, this method realizes the 6-DOF control of a robot. A vital challenge in traditional vision-based systems is avoiding local minima and singularity problems. To tackle this issue, a full-rank interaction matrix hybrid visual servo (FRHVS) design criterion is proposed, which guarantees that the hybrid interaction matrix and its pseudoinverse matrix are both full rank. Moreover, the interaction matrix for these hybrid strategies, which combines image features with other sensors features, is derived in an analytical form. Experiments on a 6-DOF manipulator show that the proposed method is effective and has global asymptotic stability and high precision.

In this study, a data-augmentation method is proposed to narrow the significant difference between the distribution of training and test sets when small sample sizes are concerned. Two major obstacles exist in the process of defect detection on sanitary ceramics. The first results from the high cost of sample collection, namely, the difficulty in obtaining a large number of training images required by deep-learning algorithms, which limits the application of existing algorithms in sanitary-ceramic defect detection. Second, due to the limitation of production processes, the collected defect images are often marked, thereby resulting in great differences in distribution compared with the images of test sets, which further affects the performance of detect-detection algorithms. The lack of training data and the differences in distribution between training and test sets lead to the fact that existing deep learning-based algorithms cannot be used directly in the defect detection of sanitary ceramics. The method proposed in this study, which is based on a generative adversarial network and the Gaussian mixture model, can effectively increase the number of training samples and reduce distribution differences between training and test sets, and the features of the generated images can be controlled to a certain extent. By applying this method, the accuracy is improved from approximately 75% to nearly 90% in almost all experiments on different classification networks.

The transfer of artistic style is a major and demanding task in computer vision. Compared with western paintings, Chinese ink wash paintings have unique characteristics that prevent existing methods from yielding satisfactory results, such as voids, brush strokes, and ink wash tone and diffusion. The main problems include: 1) the generator does not concentrate on key global features; 2) the generated paintings lose the color of the original content image; 3) the generated paintings do not have bright edges and smooth shading. In this paper, we propose Chip-SAGAN, an innovative approach to transforming real-world pictures into Chinese ink wash paintings, which is trained with unpaired photos and Chinese ink wash paintings. We introduce a self-attention module into the generator to capture global dependencies between features. Furthermore, we introduce an edge-promoting adversarial loss and a color reconstruction loss to ensure that the generated painting matches the content image’s edges and colors. The experimental results show that our method can transform real-world pictures into high-quality Chinese ink wash paintings, and surpass state-of-the-art algorithms.

Because of various problems in the production of ceramics, such as material and transportation, there exists many defects in ceramics products. Traditional methods use pixel-level segmentation to locate defects which only achieve pixel-level accuracy. In order to realize higher-precision defect localization, we propose a novel subpixel interpolation method based on the blurred edge model. Based on the analysis to the gray histogram of the defect image, the threshold segmentation method is first used to roughly locate the defect. The formula for calculation of subpixel position using interpolation method is then derived, which is faster than the traditional fitting method. Finally, the 2-D subpixel edge localization problem is disassembled 1-D calculation problems in the X-direction and Y-direction according to the direction of the defect edge gradient. The subpixel location calculation method is further given. Experiments on different defects show that the proposed method can precisely segment the defects.

Multi-target tracking application for fast-moving workpieces has drawn increasing attention in the industrial field. For the dense, fast moving workpieces with few texture features, traditional cameras get poor quality images with dynamic blur and object adhesion, which makes the detection and tracking of workpieces unreliable. However, the event camera outputs events asynchronously at a microsecond speed when the pixel intensity changes, which can capture the contours of fast-moving workpieces well. In this paper, we propose a parallel two-pipe multi-target tracking algorithm based on the event camera for fast-moving workpieces. RGB-E image obtained by fusing the RGB image and the event solves the unreliable detection caused by dynamic blur and object adhesion. The parallel mechanism ensures that the low-speed detection pipeline does not have much impact on the speed of the high-speed tracking pipeline. Hungarian algorithm is used to associate the detection results obtained by the YOLOv4-tiny detector with the tracking results obtained by the KCF tracker. A correction algorithm based on pixel speed is proposed to synchronize detection results and tracking results. Experimental results prove the proposed algorithm can achieve reliable detection and tracking performance for fast-moving workpieces.

This article presents an automated strategy to touch the injection site on zebrafish larva skin with the injection pipette tip accurately in the presence of water-depth variation, which is a crucial problem to automate zebrafish larva microinjection. The presented method consists of two parts: adaptive coordinate transformation and curve evolution for edge detection. In the first part, the impact of refraction is taken into consideration. An adaptive calibration method is developed, which enables the coordinate transformation matrix to adapt to the changing water depth. In the second part, the abovementioned calibration result is used to keep the injection pipette tip descending along the desired route. A curve-evolution-based edge detection algorithm is introduced to detect the deformation of larva skin caused by contact with the injection pipette tip. Experimental results demonstrate that high accuracy and success rates are achieved. The effect of uncertainties caused by water-depth variation and the skill requirement in manual manipulation are eliminated. The proposed contact detection strategy can be extended to microinjection for other organisms.Note to Practitioners—As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. For studying drug toxicity and disease models, exogenous substances need to be injected into zebrafish larvae. However, for both manual and automated injection, a fatal problem is that the camera on the microscope only provides 2-D positional information. It is laborious to align the pipette tip with the injection site along the$z$-axis. Moreover, due to the characteristic of stereomicroscopes, the impact of refraction at the water surface cannot be ignored. In order to address these issues, in this article, we present an adaptive calibration method and an edge detection algorithm for zebrafish larva heart injection to avoid contact failure in practical implementations.

Convolutional neural networks (CNN) have been proved to be an effective method in the field of artificial intelligence (AI), and large-scale deploying CNN to embedded devices, no doubt, will greatly promote the development and application of AI into the practical industry. However, mainly due to the space-time complexity of CNN, computing power, memory bandwidth and flexibility are performance bottlenecks. In this paper, a framework containing model compression and hardware acceleration is proposed to solve the above problems. This framework consists of a mixed pruning method, data storage optimization for efficient memory utilization and an accelerator for mapping CNN on field programmable gate array (FPGA). The mixed pruning method is used to compress the model, and data bit-width is reduced to 8-bit by data quantization. Accelerator based on FPGA makes it flexible, configurable and efficient for CNN implementation. The model compression is evaluated on NVIDIA RTX2080Ti, and the results illustrate that the VGG16 is compressed by 30× and the fully convolutional network (FCN) is compressed by 11× within 1% accuracy loss. The compressed model is deployed and accelerated on ZCU102, which is up to 1.7× and 24.5× better in energy efficiency compared with RTX2080Ti and Intel i7 7700.

To reduce the labor effort in hazardous environments like spray painting, an automatic car part spray painting machine has been set up. This article presents an intelligent location procedure for this machine. The location procedure assumes that the car part is placed with a tiny arbitrary pose, furthermore some kinds of parts under inspection undergoes through the deformation and thus it contains a nonrigid model. To tackle this problem, a workpiece positioning system works though multicamera is established first. Subsequently, a novel modified iterative closest point (ICP) algorithm is proposed which registers the nonrigid shape to the undeformed source shape in the training library. The modified ICP combines the ideas from traditional ICP and deformation estimation from bounded biharmonic weights. It solves a nonlinear cost function by using Levenberg–Marquardt algorithm. As a result, it estimates the transformation of the target point cloud with regards to the source point cloud. Additionally, it improves the accuracy of traditional ICP and increases its scope to nonrigid shapes. By employing these results in our location procedure, it can estimate the 6-DOF pose of the car part to be painted in addition to that it also estimates the deformation compared to the source cloud in the training library. This information is subsequently used to modify the guidance trajectory of the spray gun. In this article, a test case of front bumper is given, as it is commonly made of soft plastic materials and undergoes deformation under the influence of the external force. All the stated results support the efficacy of our algorithm.

In the image based visual servo, image Jacobian is vital to the system performance because it is the bridge that warps the velocity in feature space to camera velocity in Cartesian space. However, image Jacobian is sensitive to the camera intrinsic parameters, while the camera intrinsic parameter calibration error is almost unavoidable, which heavily affects the image Jacobian and visual servo process. In this paper, we find the camera intrinsic parameter invariance of the image Jacobian in our 4 DOF visual servo system. The image Jacobian of some geometry features is invariant to the camera intrinsic parameters, indicating that the disturbance in camera intrinsic parameters will not affect the convergence trajectory of those features. To further analyse camera intrinsic invariance, we proposed camera intrinsic parameter Jacobian of Image Jacobian, which can fully describe the camera intrinsic parameter invariance of the image Jacobian. The work in this paper can be used to analyse the system sensitivity to the camera intrinsic parameters. The camera intrinsic invariance is also significant for choosing the visual servo features when designing the visual servo system.

This paper presents an identification method which is based on genetic algorithm (GA) and its improved method to estimate dynamic parameters of industrial robots without load. The procedure consists of the following steps: 1) derivation of the linear form of the dynamic model of the robot according to the Lagrange equation; 2) designing of the excitation trajectory in the form of fifth order Fourier series as exciting trajectory; 3) identification, where genetic algorithm is used to find the global optimal parameters through the genetic exchange between the groups and the survival of the fittest mechanism with the minimum variance between the theoretical torque and the actual torque as the optimization criteria; 4) model validation; 5) analysis of the factors influencing the accuracy of the results in the identification process; 6) proposal of improved method. The experimental results show that the predicted torque and the measured torque obtained by the identification algorithm have a high matching degree, and the model can reflect the actual dynamic characteristics of the robot.

With the development of intelligent technologies, intelligent railway vehicles are playing an important role in modern railway transportation. To provide an accurate estimation of speed and position as feedback for the speed regulation system of intelligent railway vehicles, we propose a robust odometry algorithm based on data fusion of a low-resolution photoelectric encoder and an IMU. Firstly, the train pose measured from IMU is used to transform the gravity vector from the world frame to IMU frame, which is used to offset the ground inclination and acquire longitudinal acceleration measurements. Then, the speed measurements of the encoder are fused with the acceleration measurements in a joint data-fusion framework. Criteria based on acceleration difference is set to judge whether wheel sliding happens, and dynamically configure the weights of IMU and encoder to compensate for the deviation caused by sliding. The performance of our algorithm is verified in MATLAB simulation tests, and the experimental results demonstrate that our method outperforms traditional encoder-based and IMU-based methods in both accuracy and robustness.

Pneumatic actuators gain much popularity in many industries where there is great demand for a safety working environment and dynamic performance of a system. But the nonlinear characteristics such as friction and air compressibility add to difficulty of controlling so that constrain its wider application. In this paper, in order to overcome the disadvantage like the inaccuracy of parameters, uncertainty of the model and disturbance, a sliding mode observer with RBF neural network is proposed. The RBF neural network is designed to appropriate the nonlinear parts of the model, and the robustness of sliding mode control can guarantee the stability of control system under perturbation and model uncertainty. The stability of this algorithm is proved by Lyapunov theory. Finally, simulations done with Simulink is designed to examine the effectiveness of our algorithm. The result shows this algorithm has good performance.

The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

In this paper, the problem of robust backstepping control for a three-degree-of-freedom experimental helicopter is investigated by using output feedback. The proposed control strategy can estimate the angular velocity through a state observer and achieve the attitude tracking of the elevation and pitch angles respectively in the case of using only the angular position sensor. The system unmodeled dynamics, parameter uncertainties, and external perturbation are addressed via robust backstepping technique. It's shown by Lyapunov stability analysis that the closed-loop system can be stabilized by the proposed controller with high control accuracy. The experimental results are provided to verify the effectiveness and advantage of the proposed control methodology.

A sliding mode control method based on nonlinear disturbance observer and nominal model is proposed to track the trajectory of manipulator with uncertain interference. A dynamic model of the manipulator is established, a sliding mode control law is designed, and the stability of the system is verified by the Lyapunov stability theory. A nonlinear disturbance observer is introduced to improve the performance of the control system. The results show that the control method reduces the unmodelled dynamic errors and the influence of uncertain external interference. Furthermore, simulation results based on Matlab prove that compared with the traditional PD position control method, proposed method has higher accuracy and better robustness.

In this paper, problem of identifying and tracking the power port of remote radio unit (RRU) is addressed. The testing of RRU requires the inspection robot to insert the probes into its power and network ports. In order to solve this problem, an experimental setup of visual servoing with 6 degrees of freedom (DoF) manipulator has been established. The initial problem of recognizing and tracking the power port of RRU has been resolved using template matching and camshift tracking algorithms. Furthermore, camshift tracking algorithm has been improved to work more accurately in this application. Modified algorithm addresses the problem of swapping of major and minor axes of camshift and enhances its application to 6 DoF from 4 DoF. Experimental results have been presented to support the research claims, and computational comparison of modified tracking algorithm with camshift has been shown.

The compliant control of the robot is widely used in the Human-Machine Interface (HMI) and robot bionics. Because the system and the environment are mutually constrained, when the external environment changes, the control environment of the system will change with it, which deteriorates the control performance of the system. This article solves the problem of how to realize the compliant control with changing environment and proposes the adaptive impedance control and compensation based on a specific compliance system. We first model our compliance system by focusing on the system's internal cylinder, pressure difference transmitter its other components and then acquire its transfer function. Then we apply the impedance control and adaptive impedance control to the compliance system. Comparing these two methods, we prove that the adaptive impedance control has better tracking performance and robustness in uncertain environment. Furthermore, the stability of the adaptive impedance compliance system is proved by the Lyapunov function. Finally, we verify this algorithm with a flange experimental platform and design an experiment about contact force between the flange and different objects. The stability and practicability of the experimental algorithm are substantiated.

In this paper, we propose a force feedback scheme for Delta device to improve precision and stability in master-slave teleoperation. A simple and exact dynamical equation is created with principle of virtual work, which is easy to calculate in real-time. After analysing three items deep in dynamical equation, a reasonable strategy is designed to identify the mass of Delta mechanism. The identified parameters and dynamical equation are verified correct in ADAMS and MATLAB softwares. Simultaneously, following previous work on haptic interface, a suitable Kalman Filter algorithm is proposed to attain smooth contact force in real-time based on the impedance of environment, and simplified due to the short period of a cycle. Finally, a whole master-slave system is set up with CHAI3D toolkit, which consists of Phantom Omni, Computer and Delta device. With a force sensor mounted on the end, the contact force is measured in practice and then filtered with proposed algorithm. The final result shows that the estimated curve followed measured data well and lied at the center of original curve.

The purpose of this paper is to design an adaptive reliable H∞controller for multirate sampled-data systems. Sampled-data systems are widely adopted in the industrial process and multirate sampling is abundant in such systems. Sensor failure is one of the faults exist in the control systems, which can result in the instability. A reliable controller is proposed in this article to guarantee the stability and H∞performance of the multirate sampled-data systems when sensor failures happen. The lifting technique is used to convert a multirate system to an equivalent discrete system and the idea of the substitution is utilized to address the controller design problem based on adaptive mechanism. With the use of the mathematical model of the practical F-404 engine, an example is illustrated to prove the applicability of the proposed method.

This investigation mainly deals with positioning operation of the electric servo system and an adaptive sliding-mode-based controller via the output feedback scheme is derived for the servo actuator in the presence of the unknown model, nonlinear input, dynamic nonlinear friction and disturbances. The designed controller based on sliding-mode approach includes a LuGre model-based friction state estimator that ensures the cancelation of the friction, and a set of model parameter estimation algorithms for the servo actuator, which are derived in the sense of Lyapunov stability theorem. In addition, in response to uncertainties and disturbances with model or not in practical applications, especially for the unknown bounded ones, the adaptive law is presented to guarantee the system's robustness. Also, the chattering problem in sliding-mode control is confronted based on the fuzzy logic. In the end, the simulation examples under the continuous trajectory commands verify the robustness and performance of the proposed control strategy.

This paper deals with the problem concerning the design method of synchronous motion controllers in form of output feedback for dual linear actuators with load differences, dynamic nonlinear friction and force ripples. The authors focus on not only nonlinear friction and disturbance but also the dual motors synchronous objective. The energy upper bound for conquering disturbances is estimated and used as compensation. After that, in order to improve the robustness of the dual-motor motion plant in the presence of external disturbances, an interference rejection approach is presented. Furthermore, due to load variation which degrades synchronous tracking performance for dual motors, the controller design method based on the convex optimization scheme is proposed. The illustrative examples show that the controller can significantly improve the tracking performance under nonlinear frictions. In the meanwhile, the disturbances with known model and random one can be restrained well.

In this paper, we propose a novel design of compliant safe joint, which has flexibility when the work load exceeds a predefined threshold. The compliance is generated by a spring. We design a special transmission mechanism to convert axial motion into circumferential motion such that the linear compliance can be converted into circular one. When the end-effector of a surgical robot actuated by the compliant safe joints collides with patient's body, the compliance of the joints will protect the patient by absorbing part of the collision energy. Because of the system's special mechanical structure, the control methods should be different when it works under different states. We propose a simple algorithm to choose control methods so that the system can work both under rigid and flexible states with different controllers. We have built a prototype to validate the design and the controller.

This paper presents the development of an endoscope manipulator with passive and active structures for functional endoscopic sinus surgery (FESS). The 5-DoF passive structure has three translations and two rotations (T3R2) that allows the surgeon to manually place the endoscope near to the entry point during. The 4-DoF motorized structure (T2R2) actively controls the endoscope's position based on the surgeon's input commands. We analyze the reciprocal screw of the passive and active structures. The motion control system is based on a real-time Linux kernel that processes the commands from the surgeon and controls the manipulator's active joints. A user control interface based on an IMU fastened on the surgeon's foot is developed; this interface measures the foot's posture and through a series of gestures, it provides the desired pan/tilt/zoom motions of the camera. The developed endoscope manipulator allows the surgeon to conduct ‘two-hand’ operations while retaining direct control of the camera. We present an experimental study to validate the performance of the robotic prototype.

In this paper, we address the positioning problem of remote centre of motion (RCM) mechanisms with uncalibrated image feedback from a monocular camera. Nowadays, RCM mechanisms are widely used in minimally invasive robotic surgery due to their ability to distally rotate a tool around a fixed entry port; note that in most surgical applications, the tools are typically controlled by manual/teleoperated motion commands given by a human user. In this paper, we depart from the traditional manual control scheme and derive sensor-based methods to automatically position the manipulated tool using real-time image feedback. To this end, we first characterise the mechanism's 3-DOF configuration with the angle of the image projected tool and scalar distances between feature points. To cope with uncertainty in the camera's calibration parameters, we propose two gradient descent estimators that adaptively compute the unknown Jacobian matrix; the stability of these algorithms is proved with Lyapunov theory. Finally, we derive a kinematic image-based controller and evaluate its performance with several positioning experiments.