This paper presents a fuzzy sampled-data tracking control framework for a class of general nonlinear systems subject to unmatched disturbances, developed through a composite nonrecursive synthesis approach. First, focusing on unmatched disturbance rejection, an Euler discretization is applied to a continuous-time nonsmooth disturbance observer, resulting in a homogeneous sampled-data observer. Subsequently, by incorporating the estimated disturbances to modify the original system, a homogeneous nonrecursive sampled-data controller is directly derived via a simple coordinate transformation. In the semiglobal stability analysis, a relationship between the sampling period and the system bandwidth factor is established. Furthermore, to address the well-recognized influence of sampling period variations on system performance, a fuzzy logic criterion describing the performance-sampling period relationship is introduced. By this criterion, a fuzzy self-tuning mechanism is employed to more flexibly determine the most appropriate sampling period for the system under real-time operating conditions. Finally, the effectiveness of the proposed strategy is validated through numerical simulations and experimental studies on robot manipulators, demonstrating its practical application potential.

Automated radiology report generation is essential in clinical practice. However, diagnosing radiological images typically requires physicians 5-10 minutes, resulting in a waste of valuable healthcare resources. Existing studies have not fully leveraged knowledge from historical radiology reports, lacking sufficient and accurate prior information. To address this, we propose a Topic-Keyword Semantic Guidance (TKSG) framework. This framework uses BiomedCLIP to accurately retrieve historical similar cases. Supported by multimodal, TKSG accurately detects topic words (disease classifications) and keywords (common symptoms) in diagnoses. The probabilities of topic terms are aggregated into a topic vector, serving as global information to guide the entire decoding process. Additionally, a semantic-guided attention module is designed to refine local decoding with keyword content, ensuring report accuracy and relevance. Experimental results show that our model achieves excellent performance on both IU X-Ray and MIMIC-CXR datasets. The code is available at https://github.com/SCNU203/TKSG

This paper proposes a new variable gain sliding mode filter augmented by variable windowing for achieving smooth and reactive response over a broad range of input frequencies. The proposed filter can be seen as a synergistic combination of Kikuuwe et al.'s [1] sliding mode filter with varying gain and sliding surfaces and a novel varying-length moving-window algorithm. In all schemes, the estimated input speed is employed for rendering the filter parameters between low and high settings. The discrete-time algorithm of the proposed filter does not suffer from chattering due to implicit (backward) Euler method. The effectiveness of the proposed filter in achieving better trade-off between noise attenuation and signal preservation is validated in both simulation and experimental scenarios by using the velocity signal obtained by differentiation of quantized position data.

Ensuring safety is crucial to promote the application of robot manipulators in open workspaces. Factors such as sensor errors or unpredictable collisions make the environment full of uncertainties. In this work, we investigate these potential safety challenges on redundant robot manipulators, and propose a taskoriented planning and control framework to achieve multi-layered safety while maintaining efficient task execution. Our approach consists of two main parts: a task-oriented trajectory planner based on multiple-shooting model predictive control (MPC) method, and a torque controller that allows safe and efficient collision reaction using only proprioceptive data. Through extensive simulations and real-hardware experiments, we demonstrate that the proposed framework11Code is available at https://github.com/jia-xinyu/arm-safety. can effectively handle uncertain static or dynamic obstacles, and perform disturbance resistance in manipulation tasks when unforeseen contacts occur.

Impedance-based control represents a prevalent strategy in the powered transfemoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walking continues to be a significant challenge. This article proposes a task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, enhancing the gait symmetry between the prosthesis and intact leg. A Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint’s angle and torque in a new task. Subsequently, a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller (FSMIC) without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

Upper limb exoskeletons can greatly benefit individuals with arm weakness by helping them perform daily activities. However, existing designs do not account for forearm rotation, which is essential for many activities. As to intention-control, existing exoskeletons commonly depend on skin-attached sensors like electromyography (EMG) or force myography (FMG), which are susceptible to sensor placement and require large computational demands for signal processing. This paper explored using shear force sensors to detect users’ intention for the forearm exoskeleton. Alongside the implementation of sensors in the exoskeleton, we developed a Finite State Machine (FSM) control framework with a threshold-based online classification method. We collected sensing data of five participants’ forearm motion and set the classification threshold based on the dataset. Then, the exoskeleton executes joint motion with impedance control based on the motion intention classified in daily activities. To evaluate this framework, we implemented this approach into a microcontroller and conducted a human experiment, involving multi-joint motions, with 15 healthy participants. The results show over 97% success rate for robot motion execution, and no significant difference in joint coordination and forearm muscle activation with and without exoskeleton. This approach with FSM and threshold-based classification allows users to control robot joints in real time according to their intentions while ensuring their safety through predefined control logic and impedance control.

Robots grasping objects are critical capabilities in warehouse environments and industrial settings. A robotic grasp generally occurs in a scenario where it is unfeasible for a worker to efficiently complete a tedious task, such as picking food and drink cans (cylinder-shaped) and packaging boxes (cube-shaped). It is worth noting that the tops of cylinders and cubes can be represented by ellipses and rectangles in the two-dimensional (2D) space, respectively. Therefore, a robot can grasp cylinder-shaped and cube-shaped objects by ellipse and rectangle detection. However, it faces the challenge of how to accurately detect cylindrical and cubic objects in real-time for robot grasping. To tackle the above research problem, we propose a grasping system that enables a robot to grasp cylinder-shaped and cube-shaped objects in static and dynamic environments by the proposed ellipse and rectangle detector. An end-to-end learning model is constructed to first incorporate a one-stage detection backbone and then, accommodate the proposed adaptive multi-branch multi-scale net with a designed iterative feature pyramid network, local inception net, and multi-receptive-field feature fusion net to generate object detection recommendations. Employing depth information, the coordinates of detected objects are converted to the 3D space via sampling a series of registered depths and pixels on objects from the live video stream. Comparisons with recent detection methods on the same dataset indicate that the proposed ellipse and rectangle detectors present better performance. Abundant grasping experiments are conducted to illustrate that a robot, empowered by the proposed detector, has the capability of grasping cylindrical and cubic objects in dynamic scenarios. (Video on YouTube,https://youtu.be/KK1OtW6GvL0). Note to Practitioners—This paper is motivated by the problem of how to enable a robot to grasp objects with the basic geometric primitives-ellipses and rectangles in static and dynamic scenarios. Our target is to provide a potential solution for flexible industrial settings in operating moving cylinder-shaped and cube-shaped objects (food and drink cans and packaging boxes) in dynamic scenarios such as conveyors of production lines and logistics lines. We constructed a supervised learning model that can accurately and quickly detect ellipses and rectangles. Through the verification of the comparisons with recent methods and robotic grasping experiments, the behavior of the proposed method can be used in practical applications. In the future, we will deploy this robotic grasping system based on the proposed perception method to grasp food and drink cans and packaging boxes from moving conveyors on production lines and logistics lines.

The cable-driven upper-limb exoskeleton robot with compliant actuators presents notable advantages when applied to rehabilitation training, yet introduces challenges in position tracking control. Traditional control methods inadequately address hysteresis issues caused by cable drives and the performance degradation resulting from compliant actuators. To solve these common issues in such robotic systems, this study introduces a unified dual closed-loop control strategy that aims to enhance system tracking performance. In the outer loop, an adaptive hysteresis compensator is intricately designed to address position errors induced by cable-driven motion from a kinematic perspective. Simultaneously, the inner loop employs a high-order controller utilizing the backstepping method to control the dynamics of the robotic system with flexible joints effectively. Additionally, a dedicated high-order super-twisting observer (STO) is integrated to estimate unknown dynamic models and external disturbance, enabling the generation of disturbance rejection items for the inner-loop controller design. Experimental results validate the efficacy of the proposed control strategy, demonstrating commendable tracking performance when compared with alternative methods.

While the hybrid driving method effectively addresses the contradiction between inherent compliance and the finite load-bearing capability of continuum robots, integrating multiple actuations poses challenges in modeling and control. This article introduces a dynamics-based robust uncertainty estimation and control (DRUEC) method for hybrid-driven continuum robots with continuously variable stiffness to tackle the fast internal dynamic variations and enhance motion tracking accuracy. Initially, a conventional kinematic formula is established to transfer all local force and position vectors into the global coordinate. Subsequently, the Euler-Lagrange methodology is employed to construct the entire dynamic model within the actuation space. For improving programming efficiency and independence from system parameter identification technologies, explicit expressions of matrices in the constructed dynamic model are derived by using the chain rule and properties of homogeneous coordinate transformation. In addition, a novel robust uncertainty estimator (RUE) is proposed to estimate modeling errors arising from the unmodeled dynamics and parameter perturbations. Various experiments are implemented based on a hybrid-driven continuum robot with two segments. Comparative results show the effectiveness of the proposed scheme over the classical methods. Note to Practitioners—The motivation for this article is to enhance the motion tracking accuracy of hybrid-driven continuum robots. Due to their inherent compliance, continuum robots exhibit a great adaptation capability to restricted environments. However, a conflict between intrinsic compliance and positioning accuracy of the endpoint limits their practical applications. To tackle this issue, the hybrid driving method offers an accessible solution by decoupling stiffness regulation from position adjustment. This enables different stiffness levels at the same posture, allowing the continuum robot to withstand varying loads without experiencing significant deformations. Nevertheless, the incorporation of multiple actuations also complicates modeling and control due to the increased coupling and nonlinearity. Moreover, the effective operation of continuum robots needs to timely deal with the effects induced by the rapid internal dynamic changes and relatively large movement speeds. These facts imply that the commonly used quasi-static models or kinematics-based methods are insufficient. Therefore, this article is dedicated to improving the control accuracy from the perspective of dynamics-based control approaches.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. We presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait phase and predict gait percentage from IMU signals. Experiments were conducted to detect the four lateral walking gait phases and predict their percentage across different speeds. The performance of SDA-NN-ML and Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) and Long Short Term Memory (LSTM) were evaluated. The cross-subject recognition accuracy of SDA-NN-ML (89.94%) decreased by 4.62% compared to the training accuracy, which outperformed SVM (8.60%), AdaBoost (5.61%), and LSTM (7.12%). For real-time and cross-subject prediction of gait phase percentage, the RMSE of SDA-NN-ML (0.2043) outperformed that of a single regression network (0.2426). With a signal noise ratio of 100:30, the cross-subject recognition accuracy decreased by a mere 5.70%, while the prediction result (RMSE) of SDA-NN-ML increased by 0.0167 when compared to the noise-free results. SDA-NN-ML demonstrates a stable multi-step-ahead prediction ability with an accuracy higher than 82.50% and an RMSE of less than 0.23 when the ahead time is less than 200 ms. The results demonstrated that the proposed method has high accuracy and robust performance in lateral walking gait recognition and prediction.

Accurate segmentation of liver tumors is crucial for their proper diagnosis and treatment. However, achieving high levels of precision typically depends on meticulous manual annotation, a process that is not only labor-intensive but also constrained by the scarcity of large-scale, real-world datasets. These datasets are indispensable for the training and validation of segmentation algorithms. Furthermore, the liver’s considerable variation in size, shape, and pathology type poses a challenge in collecting a sufficient number of image samples that adequately represent this diversity. To surmount the challenges of the time-consuming manual annotation process and the limitations in data acquisition, there is an urgent need for the development of an efficient and comprehensive tumor generation method. Some existing approaches, such as those utilizing Gaussian blurred ellipses to simulate tumors, fail to accurately reflect the biological complexity and pathological diversity of liver tumors. In this article, we introduce an innovative tumor synthesis method Latent Diffusion Model for Pathology (LDMP) that leverages medical imaging knowledge to more accurately replicate the intricacies of liver tumor morphology and pathology. This approach aims to enhance the quality and diversity of training data, thereby improving the performance of segmentation algorithms and ultimately contributing to more precise diagnoses and treatments. The method uses deep learning techniques, particularly diffusion models, to simulate real liver CT images and incorporates the biological properties of tumors into the synthesis process to generate realistic tumor images. The quality of the synthetic images is assessed using Principal Component Analysis (PCA) and Kullback–Leibler (KL) divergence to ensure the authenticity of the tumor’s spatial structure. Experimental results show that the proposed method can significantly improve the Dice Similarity Coefficient (DSC) of the tumor segmentation model and enable researchers to freely define the size and blur degree of the tumor, thereby creating medical images with precise annotations. In addition, we introduce a self-checking step before the output of synthetic data, which provides a new paradigm in the field of image synthesis and effectively compensates for potential errors in synthetic data. Our approach not only provides an effective solution for medical image analysis but also provides high-quality synthetic image resources for medical education and clinical practice. Codes are available at: https://github.com/jhf0721/tumor .

To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the convergence of tracking errors and strengthen anti-perturbation ability. The former aims to enhance feedback portions through error-driven control gains, while the latter exploits finite-time convergence of discontinuous switching terms. We analyze the boundedness of control signals and the stability of the closed-loop system in theory, and conduct real hardware experiments on a robot manipulator with seven degrees of freedom (DoF). Experimental results verify the effectiveness and improved performance of the proposed controllers, and also show the feasibility of implementation on high-dimensional robots.

Parameter convergence in adaptive control is crucial for improving the stability and robustness of robotic systems. Nevertheless, a stringent condition named persistent excitation (PE) needs to be satisfied to ensure parameter convergence in the conventional adaptive robot control. Composite learning robot control (CLRC) is an innovative methodology that guarantees parameter convergence under a condition of interval excitation (IE) that is strictly weaker than PE. This paper puts forward a time-division multi-channel (TDMC) CLRC strategy such that parameter convergence is achieved even without the IE condition. In the TDMC mechanism, a filtered regressor is integrated with multiple time intervals to generate a generalized prediction error for parameter update, such that excitation information of regressor channels at different instants is exploited more effectively and efficiently to achieve fast and accurate parameter estimation. Global exponential stability with parameter convergence of the closed-loop system is achieved under a partial IE condition that is much weaker than IE. Experiments on a collaborative robot with 7 degrees of freedom have demonstrated the superiority of the proposed approach in both parameter estimation and trajectory tracking compared to start-of-the-art approaches.

The existing inverse optimal methods for canonical nonlinear systems assume that the system is modeled precisely and accurately, but dynamic uncertainties commonly exist and are unavoidable and difficult to model in practical engineering and industrial systems. This work removes this limitation and solves the problem of inverse optimal adaptive control for canonical nonlinear systems with dynamic uncertainties. Technically, a criterion on inverse optimality under dynamic uncertainties is newly proposed based on a new auxiliary system and a meaningful cost functional. With the new criterion, a robust adaptive fuzzy inverse optimal control scheme is proposed to design an inverse optimal controller, which, however, is not necessarily a stable controller. To solve this issue, a projection-based adaptation law is proposed to update the inverse optimal controller. Then, a small-gain approach is proposed to construct the links between inverse optimality and stability and to render that the closed-loop system is input-to-state practically stable. The proposed methods are successfully applied to industrial robots for demonstrations.

Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing works on solving this problem usually require a complicated and time-consuming learning procedure by employing neural networks, and the results are only applicable for simple or time-invariant constraints. In this work, these restrictions are removed by a newly proposed adaptive neural inverse approach. In our approach, a new universal barrier function, which is able to handle various dynamic constraints in a unified manner, is proposed to transform the constrained system into an equivalent one with no constraint. Based on this transformation, a switched-type auxiliary controller and a modified criterion for inverse optimal stabilization are proposed to design an adaptive neural inverse optimal controller. It is proven that optimal performance is achieved with a computationally attractive learning mechanism, and all the constraints are never violated. Besides, improved transient performance is obtained in the sense that the bound of the tracking error could be explicitly designed by users. An illustrative example verifies the proposed methods.

Compared to conventional back-support exoskeletons (BSEs) with two motors, BSEs driven by a single motor have the advantage of light weight. However, current single-motor BSEs have problems with accommodating asynchronous hip movements, achieving precise force control and efficient force transmission, and giving autonomy to users when walking. In this article, we propose a novel BSE with a differential series elastic actuator (D-SEA) for lifting assistance. The unique differential working principle can accommodate the angular difference between the hip joints and provide the same assistive torque at both hip joints. The D-SEA achieves precise force control with a custom controller based on accurate spring deflection feedback, and drives the hip joints via an efficient cable-roller mechanism. Taking advantage of the active backdrivability of the D-SEA, we proposed an intelligent assistive strategy that automatically provides adequate support for lifting tasks and grants autonomy to users during walking. In experiments, the BSE reduced the activation level of the back muscles by up to 40% during lifting, without increasing the activation of the back and leg muscles during walking.

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Series elastic actuators (SEAs) have been the most popular compliant actuators as they possess a variety of advantages, such as high compliance, good backdrivability, and tolerance to shocks. They have been adopted by various rehabilitation robots to provide appropriate assistance with suitable compliance during human–robot interaction. For a multijoint SEA-driven rehabilitation robot, a big challenge is to develop an assist-as-needed (AAN) method without losing stability during uncertain physical human–robot interaction. For this purpose, this article proposes a human–robot interaction evaluation-based AAN method for upper limb rehabilitation robots driven by SEAs. First, in order to stabilize the SEA-level dynamics, singular perturbation theory is adopted to design a fast time-scale controller. Second, for the robot-level dynamics, an iterative learning algorithm is adopted for impedance adaption according to the task performance and human intention. The interaction force feedback is introduced for human–robot interaction evaluation, and the intensity of robotic assistance will be adjusted periodically according to the evaluation results. The stability of human–robot interaction is provided with the Lyapunov method. Finally, the proposed rehabilitation method is constructed and implemented on a two-degree-of-freedom SEA-driven robot. It handles the uncertain interaction in such a principle that correct movements will lead to less assistance for encouraging participation and incorrect movements will lead to more assistance for effective training. The proposed method adapts to the subject's intention and encourages higher participation by decreasing impedance learning strength and increasing allowable motion error. It can fit the participants with different motor capabilities and provide adaptive assistance when a specific trainee tries to change his/her participation during rehabilitation. The performance of the AAN method was validated with experimental studies involving healthy subjects.

Most human activities in daily living or professional work rely on upper body motion. Measuring upper body motion is essential for many applications such as health evaluation, rehabilitation, human power augmentation, skill transferring, etc. Computer vision-based systems have been widely used to directly capture upper limb motion but are usually constrained in a restricted area. Wearable sensors such as inertial measurement units (IMUs) are promising to enable ambulant and out-of-lab measurements but also suffer from issues such as magnetic distortion and drifting. Some visual-inertial systems have been proposed recently to fuse these two complementary measurements but mostly apply in a restricted area. In this paper, we propose a fully wearable egocentric visual-inertial system to estimate the upper-limb pose. Magnetometers are not used to allow the system to work in complex industrial and daily living scenarios or to be integrated with motorized assistive devices. Methods to automatically calibrate the sensor-to-segment alignment and estimate upper body motion is presented and validated with an optical motion capture system. Experimental results showed the system can estimate the joint angles without drift and obtain accurate wrist position even with occlusion, verifying the efficacy of the proposed system and method.

Finite-time control is concerned with steering a system state to the origin before a certain settling-time limit, ignoring any consideration of when each state element converges relative to the others. In this article, a control problem called time-synchronized control is investigated, where all the system state elements have to converge to the origin at the same time. To facilitate this problem formulation, we introduce the notion of time-synchronized stability together with sufficient Lyapunov conditions. Based on these, the analytical solution of a time-synchronized stable system is obtained and discussed, explicitly offering a quantitative method to preview and predesign the control system performance in prior. Following these results, a robust time-synchronized control law is designed for multivariable systems under external disturbances and model uncertainties. Finally, comparative numerical simulations between time-synchronized control and finite/fixed/prescribed-time control are conducted to showcase the time-synchronized features attained.

Most existing methods on optimal finite-time control are restricted to a complex design and learning procedure, and only practical finite-time stable is ensured, which greatly limits the desirable performance of optimal and finite-time control. To solve the problem, an adaptive fuzzy fixed-time inverse approach is first proposed in this article, which achieves the optimized performance without recourse to Hamilton–Jacobi–Bellman equations and improves practical finite/fixed-time stable to fixed-time stable. Technically, to overcome the inverse optimal design difficulty of a nonlinear fixed-time controller, a series of singularity-avoidance functions and a Sontag-type function are incorporated to design a specified form of auxiliary controller, based on which an inverse optimal fixed-time controller is designed. Then, by introducing a two-Lyapunov functions method, it is proved that inverse optimal stabilization is ensured and the tracking error goes to a prescribed interval asymptotically within a fixed-time. Effectiveness of the proposed methods are illustrated by two examples.

OBJECTIVE: Accurate real-time estimation of motion intent is critical for rendering useful assistance using wearable robotic prosthetic and exoskeleton devices during user-initiated motions. We aim to evaluate hierarchical classification as a strategy for real-time locomotion mode recognition for the control of wearable robotic prostheses and exoskeletons during user-initiated motions. METHODS: We collect motion data from 8 subjects using a set of 7 inertial sensors for 16 lower limb locomotion modes of different specificities. A CNN based hierarchical classifier is trained to classify the modes into a specified label hierarchy. We measure the accuracy, stability, behaviour during mode transitions and suitability for real-time inference of the classifier. RESULTS: The method achieves stable classification of locomotion modes using [Formula: see text] of time history data. It achieves average classification accuracy of 94.34% and an average AU(PRC) of 0.773 - comparable to similar classifiers. The method produces more informative classifications at transitions between modes. Less specific classes are classified earlier than more specific classes in the hierarchy. The inference step of the classifier can be executed in less than 2 ms on embedded hardware, indicating suitability for real-time operation. CONCLUSION: Hierarchical classification can achieve accurate detection of locomotion modes and can break up mode transitions into multiple transitions between modes of different specificity. SIGNIFICANCE: Multi-specific hierarchical classification of locomotion modes could lead to smoother, more fine grained control adaptation of wearable robots during locomotion mode transitions.

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), whereat the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also anorm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

Motion control of multi-joint Series Elastic Actuator (SEA)-driven robots still faces challenges including intrinsic oscillatory dynamics, high-order robotic dynamics, low-bandwidth inner loop, and dynamic nonlinearities. In this letter, a nonlinear disturbance observer (NDOB)-based robust controller with the singular perturbation theory is proposed to perform stable and precise motion control of multi-joint SEA-driven robots. First, a fast-time control term is designed according to the singular perturbation theory to stabilize the SEA-level dynamics. Then, for the robot-level dynamics, a NDOB is designed to estimate the effects of unmodeled dynamics and external disturbance. The NDOB is combined with a baseline computed torque controller (CTC) to construct a composite controller NDOB-CTC. In addition, bounded stability is achieved with Lyapunov-type analysis. Finally, the proposed controller was implemented on a 2 DOFs SEA-driven robot. Comparative experiments were conducted for validations.

Series elastic actuators (SEAs) can provide accurate force control and backdrivability in physical human-robot interaction. Control of SEA-generated forces or torques makes allowance for the user's own volitional control and allows implementing a wide variety of assistive strategies. A novel force control method for a SEA-driven lower-limb assistive exoskeleton is presented. The device features variable-structure SEAs coupled via Bowden cables. The actuator alternates between two discrete levels of stiffness depending on the amplitude of the commanded force. The algorithm features a switching force-tracking control based on the forward-propagating Riccati equation. A disturbance-rejection component increases the device's transparency in zero assistance mode. The force control was used to implement an assistive strategy that aims to correct the asymmetric gait typical of stroke survivors. Assistive joint torques synchronize with the user's gait by means of an adaptive frequency oscillator, which extracts the continuous phase and frequency of the patient's gait using data from both the paretic and the healthy sides. The control was tested with healthy subjects wearing the exoskeleton while subject to a simulated knee flexion impairment. The control proved effective in restoring spatial and temporal knee flexion symmetry to levels comparable to unobstructed gait.

This paper studies the stability analysis of the input saturated discrete-time switched systems with average dwell-time based on the parametric discrete-time Riccati equation. The state feedback controller and the observer-based output feedback controller are designed to guarantee the exponential stability of the closed-loop system. The proposed method is simple and easy to operate in practice. The designed controllers can be computed easily by solving the parametric discrete-time Lyapunov equation. The main advantages of this paper are that the stability analysis is based on the properties of the saturation function and limited structure information of the actuator saturation and that the designed controllers have good robustness to the structure uncertainty of the input saturation. The simulation results illustrate the effectiveness of the proposed methods.

Soft robot has demonstrated promise in unstructured and dynamic environments due to unique advantages, such as safe interaction, adaptiveness, easy to actuate, and easy fabrication. However, the highly dissipative nature of elastic materials results in small stiffness of soft robot which limits certain functions, such as force transmission, position accuracy, and load capability. In this paper, we present a novel articulated soft robot with variable stiffness. The robot is constructed by rigid joints and compliant bistable structures in series. Each joint can be independently locked through triggering the bistable structure to touch the mechanical constrain. Thus, the bending stiffness of the joint can be magnified which increases the stiffness of the articulated soft robot. Through this construction method, even driven by only one servomotor, the robot demonstrates variable workspace and stiffness which have the potential of dexterous manipulation and maintaining shape under tip load.

To function effectively in real-world environments, powered wearable robots such as exoskeletons and robotic prostheses must recognize the user's motion intent by detecting the user's locomotion modes such as walking, stair ascent and descent or ramp ascent and descent. Traditionally, intent detection is achieved using rule based methods such as state machines or fuzzy logic using data from wearable sensors. Due to the difficulty of manual rule design, these methods are limited to detect certain simple locomotion modes. Machine learning (ML) based methods can perform classification on a large number of classes without manual rule design and recent research has explored several ML methods for locomotion mode classification. However, current ML based methods for locomotion mode detection use classical methods that require use of feature engineering to achieve acceptable accuracies. Additionally, current ML strategies only classify when certain motion events are detected. This strategy, while computationally efficient could result in misclassifications affecting large sections of motion recognition. To overcome these limitations, this paper proposes an end-to-end deep learning based method for locomotion mode detection that eliminates the need for feature engineering and classifies at a fixed sample rate. This paper introduces a new metric called confidence index and proposes a strategy for tuning confidence index thresholds to achieve a stable intent recognition and overall accuracy of greater than 95% on a publicly available benchmark dataset.

We present a differential-algebraic formulation with switching constraints to model the nonsmooth dynamics of robotic systems subject to changing constraints and multiple impacts. The formulation combines a single structurally simple governing equation, a set of switching kinematic constraints, and the plastic impact law, to represent the dynamics of robots that interact with their environment. The main contribution of this formulation is a novel algorithmic impact resolution method which provides an explicit solution to the classical plastic impact law in the case of multiple simultaneous impacts. This method serves as an alternative to prior linear-complementarity-based formulations which offer an implicit impact resolution through iterative calculation. We demonstrate the utility of the proposed method by simulating the locomotion of a planar anthropometric biped.

This paper investigates a global decentralized nonsmooth tracking algorithm for a class of interconnected nonlinear systems with strongly coupled interactions. As a main contribution, a nonrecursive dynamic exact tracking control design is proposed for the decentralized control issue which facilitates an intrinsic separation of control law design and stability analysis. First, a fully decentralized extended high-gain observer is constructed to enable the dynamic controller design and performance recovery with the presence of additional disturbances. Then by integrating a nonrecursive homogeneous domination strategy, now the decentralized tracking control law can be designed in a very simple and explicit manner whereas the control gains follow the conventional pole placement approach. Moreover, the nonsmooth design framework will render a finite-time convergence rate of the output tracking which is of significance in practices. The effectiveness of the controller is demonstrated by a rigorous stability analysis and simulation verifications.

In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed and a new estimation error is used to update the augmented weights. Based on the identification results, two singularity-free NN indirect adaptive controllers are developed for nonlinear systems with unknown constant control gains or variable control gains, respectively. Because the singularity problem is eradicated, the proposed methods remove limitations on parameter estimates that are used to guarantee the positiveness of the estimated control gain. Consequently, a more accurate estimation result can be achieved and the system state can track the given reference signal more precisely. The effectiveness of the proposed identification and control algorithms are tested and the superiority of the proposed singularity-free approach is demonstrated by simulation results.

Emerging applications of robots with direct physical interactions with humans have led to the development of a variety of series elastic actuators (SEAs) which are compliant, force controllable, and back drivable. The performance of current SEAs is mainly dependent on the specific stiffness of the spring. In our previous work, a compliant actuator with two-stage stiffness has been designed to overcome the performance limitations in current SEAs. The key novelty is that a low-stiffness spring and a high-stiffness spring are employed instead of a single spring in current SEAs, which has the advantages of high fidelity, low output impedance, and also large force range and bandwidth. In this paper, a tracking control scheme is proposed for the compliant actuator with two-stage stiffness. Although the overall stiffness is discontinuous, the proposed controller is continuous by integrating different control modes for two springs into a single one. The transition between control modes is smooth and embedded inside the controller, and it is also automatically realized by monitoring the output force of the actuator. The stability and convergence of the closed-loop system are analyzed, and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.Note to Practitioners—An SEA is developed by placing an elastic element into the actuator; this elasticity gives SEAs several unique properties including low mechanical output impedance, tolerance to impact loads, and passive mechanical energy storage, which makes it suitable for human–robot interaction. The performance of existing SEAs is highly dependent on the stiffness of a single spring. To overcome the limitations, a novel SEA with two-stage stiffness was proposed in our previous work. This paper suggests a continuous tracking control method for the proposed compliant actuator. Although the overall stiffness is discontinuous, the transition between different control modes for two springs is smooth and automatically realized. Experimental results show that the output force of the actuator is bounded. In future research, uncertainties in actuator dynamics will be considered, such that system identification or calibration is not required.

Sliding mode control (SMC) is attractive for nonlinear systems due to its invariance for both parametric and nonparametric uncertainties. However, the invariance of SMC is not guaranteed in a reaching phase. Integral SMC (ISMC) eliminates the reaching phase such that the invariance is achieved in an entire system response. To reduce chattering in ISMC, it was suggested that the switching element is smoothed by using a low-pass filter and an integral sliding variable is modified. This study discusses several crucial problems regarding the performance, modification, and improvement of ISMC. First, the modification of the integral sliding variable is revealed to be unnecessary as it degrades the performance of a sliding phase; second, ISMC is shown to be a kind of global SMC; third, it is manifested that a high-order ISMC design with super twisting involves a stability condition that may be infeasible in theory; finally, an efficient solution is suggested to attenuate chattering in ISMC without the degradation of tracking accuracy and the solution is extended to the case with uncertain control gain functions. Comprehensive simulation results have verified the arguments of this study.

One weakness of the model predictive control method is that the predicted states/outputs are constructed by an exact nominal model. Its accuracy varies if uncertainties exist, which will ultimately deteriorate the closed-loop control performances. To this end, we propose a generalized dynamic predictive control method for a class of lower-triangular systems subjected to nonparametric uncertainties. Instead of relying on the inherent robustness property of the standard predictive controller or on-/off-line parameter identification, a dual-layer adaptive law is designed to estimate the lumped effect of system uncertainties. As another main contribution, under a less ambitious but more practical control objective, namely semi-global stability, various nonlinearity growth constraints utilized in the existing related methods could be essentially relaxed. Numerical simulation and illustrative experimental tests of a series elastic actuator system are provided to demonstrate both simplicity and effectiveness of the proposed method.

Patient transportation in hospitals faces many challenges, including the limited manpower, work-related injuries, and low efficiency of current bed pushing methods. This paper presents a new motorized robotic bed mover with omnidirectional mobility to address this problem. This device is composed of an omnidirectional mobility unit, a force sensing-based human-machine interface, and control hardware with batteries and electronics. The proposed bed mover can be attached to the bottom of a manual hospital stretcher, transforming it into a powered omnidirectional bed (OmniBed) that can be used only by one person. The function of the OmniBed is compared with that of a conventional powered bed, which only provides forward assistance with a fifth powered wheel. We perform a pilot study with 14 subjects to evaluate the performance of this OmniBed and benefits for hospital application. The experimental results show that the OmniBed can half the manpower while decreasing back muscle activities, revealing the potential health benefits for older staffs. The OmniBed also shows the promising signs of high precision and handling in small spaces with its one-step "parallel-parking" ability. This device is more ergonomic, more effective, and safer than the conventional powered bed.

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to realize the power assistance. The positions of the electrodes where the motion information of shoulder joint is effectively and exactly extracted are discussed, and a linear model that correlates the EMG to the EEG signal is constructed utilizing motion-related features extracted from multi-location EEG measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG using Principal Component Analysis (PCA) method. The proposed approach is experimentally verified, and an average correlation coefficients are as high as about 0.90 for different subjects are obtained between the estimated and the actually measured EMG signal. Our results suggest that the estimation of EMG based on EEG is feasible. This demonstrates the potential of using EEG signals to support human activities via brain-machine interface.

In this paper, a new robust position controller is proposed for Series Elastic Actuators (SEAs) by using Sliding Mode Control (SMC) and a second order Disturbance Observer (DOb). The latter estimates not only disturbances but also their first and second order successive derivatives. A simple yet efficient dynamic model of the position control system is derived by using the analogy of a two-mass-spring-damper system. It is of fourth order and suffers from collocated and non-collocated disturbances. The former is directly cancelled by feeding-back its estimation through control input. The latter is suppressed by treating the estimations of disturbances and their first and second order time derivatives in the design of the SMC-based robust position controller. By cancelling disturbances via their estimations, not only the robustness of the position control system is improved but also the control signal chattering is lowered. The proposed robust controller significantly improves the position control performance of SEAs by suppressing plant uncertainties and external disturbances, such as friction, backlash, inertia variation and load. The validity of the proposed robust position controller is verified by giving experimental results of an SEA.

Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI systems. We aimed to propose a BCI-based system with a visual servo module to operate a service robot. We recorded single-channel steady-state visual evoked potentials (SSVEP) as input signals for the BCI system of this study. The visual stimuli for inducing SSVEP were modulated at seven different frequencies with the sampled sinusoidal method. Correspondingly, this SSVEP-based BCI system can generate seven control commands for the operation of the service robot, which can provide three fundamental services: mobility, manipulation, and delivery. The visual servo module was established to reduce the burden of users and accelerate service procedures. To evaluate the performance of this system, subjects were recruited to participate in the experiments. All the participants succeed in operating the robot to provide the basic services. According to the experimental results, this SSVEP-based BCI system that incorporates the visual servo module can be effectively used to operate service robots with reduced number of channels and increased ability to perform multiple tasks.

In this paper, a novel walker robot is proposed for post-stroke gait rehabilitation. It consists of an omni-directional mobile platform which provides high mobility in horizontal motion, a linear motor that moves in vertical direction to support the body weight of a patient and a 6-axis force/torque sensor to measure interaction force/torque between the robot and patient. The proposed novel walker robot improves the mobility of pelvis so it can provide more natural gait patterns in rehabilitation. This paper analytically derives the kinematic and dynamic models of the novel walker robot. Simulation results are given to validate the proposed kinematic and dynamic models.

A Series Elastic Actuator (SEA) is designed by placing a passive compliant element between a conventional stiff actuator and link. The intrinsically compliant mechanical structure provides several superiorities, e.g., safety, energy efficiency, high force fidelity, low cost force measurement, high transparency, etc., in advanced robot applications, such as humanoids, quadrupeds and exoskeletons. However, the motion control problem of an SEA is more complicated than that of a conventional stiff actuator due to its higher order dynamics. This paper proposes a novel Active Disturbance Rejection (ADR) based robust force controller for SEAs by combining Differential Flatness (DF) and Disturbance Observer (DOb) in state space. The robust state and control input references are systematically generated in terms of a fictitious variable, namely differentially flat output, estimated disturbances and their successive derivatives. A second order DOb is designed in state space so that disturbances and their first and second order derivatives are estimated. It is experimentally shown that high performance force control applications can be performed without requiring the precise dynamic models of the actuator and environment when the proposed robust force controller is implemented.

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Series elastic actuators (SEAs) are known to offer a range of advantages over stiff actuators for human–robot interaction, such as high force/torque fidelity, low impedance, and tolerance to shocks. While a variety of SEAs have been developed and implemented in initiatives that involve physical interactions with humans, relatively few control schemes were proposed to deal with the dynamic stability and uncertainties of robotic systems driven by SEAs, and the open issue of safety that resolves the conflicts of motion between the human and the robot has not been systematically addressed. In this paper, a novel continuous adaptive control method is proposed for SEA-driven robots used in human–robot interaction. The proposed method provides a unified formulation for both therobot-in-chargemode, where the robot plays a dominant role to follow a desired trajectory, and thehuman-in-chargemode, in which the human plays a dominant role to guide the movement of robot. Instead of designing multiple controllers and switching between them, both typical modes are integrated into a single controller, and the transition between two modes is smooth and stable. Therefore, the proposed controller is able to detect the human motion intention and guarantee the safe human–robot interaction. The dynamic stability of the closed-loop system is theoretically proven by using the Lyapunov method, with the consideration of uncertainties in both the robot dynamics and the actuator dynamics. Both simulation and experimental results are presented to illustrate the performance of the proposed controller.

Series elastic actuators (SEAs) are known to offer a number of advantages such as high force/torque fidelity, low impedance, and tolerance to shocks, which make it suitable for the applications involving human-robot interaction. In existing SEA-driven robot systems, the control objective is usually specified as a predefined trajectory or an impedance model that describes the relationship between the desired motion of robot and the external force, and controllers are always activated to regulate the desired motion or the desired impedance model. In this paper, a region control scheme is proposed for robots driven by SEAs, where the control objective is specified as a region, instead of trajectory or desired impedance. The region control has the advantage of flexibility, in the sense that the robot is able to move freely inside the desired region and thus compliant with the environment or physical interactions with humans. Though the overall dynamics that includes both actuator and robot dynamics is a fourth-order system, the proposed control method does not require the high-order derivatives or the construction of any observer. Experimental results are presented to demonstrate the effectiveness of the proposed control method.

This paper proposes a new state-space controller for the robust trajectory tracking control problem of multi-mass resonant systems. It is designed in state-space by combining Differential Flatness (DF) and a higher order Disturbance Observer (DOb). The former systematically generates the control input and state references of a state feed-back controller so that not only regulation but also trajectory tracking control can be performed in state-space. They are obtained in terms of differentially flat output variable in DF. The latter estimates disturbances and their successive time derivatives. Its order depends on the resonant modes of the system. The proposed controller has a two-degrees-of-freedom motion control structure. Its performance and robustness can be independently adjusted by tuning the state feed-back controller and DOb, respectively. It is designed in two steps. In the first one, the state feed-back controller is tuned by only considering the nominal plant model of the system. The nominal performance is adjusted by using pole placement; e.g., all poles of the nominal system are placed on the real axis so as to suppress the vibration at tip. The state and control input references of the state feed-back controller are obtained by using DF. In the second step, the robustness of the system is achieved by reconstructing the state vector via the estimations of disturbances and their successive time derivatives. Thanks to the proposed new state variables, mismatched disturbances are automatically cancelled by the state feed-back controller. The matched disturbances are cancelled by feeding-back their estimations through control input. Hence, the robustness of the multi-mass resonant systems is achieved. Without losing generality, the proposal is verified by considering three-mass resonant systems. Its simulation results are given to validate the proposal.

In this paper, a new robust force controller is proposed for Series Elastic Actuators (SEAs) by using conventional Sliding Mode Control (SMC) and Disturbance Observer (DOb). The position measurement of the actuator's link is subtracted from the desired deflection of the spring so that the force control goal is defined as the desired position of the motor; i.e., the force control is simply performed by designing a position controller at motor side. However, the position accuracy of the motor is influenced by the dynamics of the actuator and environment. Since they cannot be easily identified in practice, the dynamics of the actuator's link and environment are considered as unknown disturbances in the design of the proposed controller. In order to improve the robustness, conventional SMC-based robust force controller is designed without considering the unknown disturbances. Although the robust force control can be theoretically performed by using the conventional SMC-based controller, it suffers from chattering in real implementations. Conventional DOb-based robust motion controller is designed at motor side so that not only the disturbances are cancelled by feeding-back their estimations but also the control signal of SMC-based robust force controller is lowered. Hence, a simple yet efficient robust force controller is designed for SEAs. The validity of the proposed robust force controller is verified by giving experimental results.

Series Elastic Actuators (SEAs) have several superiorities over conventional stiff and non-back-drivable actuators in force control, e.g., lower reflected inertia, low cost force measurement, high force fidelity, safety, and so on. However, their position control applications significantly suffer from low performance and disturbances due to insufficient controller designs. In this paper, a new Active Disturbance Rejection (ADR) controller is proposed for the robust position control problem of SEAs by combining Differential Flatness (DF) and Disturbance Observer (DOb) in state space. The trajectory of the actuator is generated by using DF and is tracked by using a conventional state feed-back controller. The state and control input references of a DF-based trajectory tracking controller are modified by using estimated disturbances so that the robustness is achieved. The proposed controller provides high performance position control for SEAs when they suffer from plant uncertainties and external disturbances such as inertia variation, backlash, friction and external load. Experimental results are given to validate the proposal.

Series elastic actuator (SEA) has been widely used in rehabilitation robotics, where human-robot interaction is required. Due to its intrinsic compliance, SEA can improve the usage of power for its motor, which leads to a compact and lightweight SEA design. The aim of this paper is to reduce the energy consumption and the power requirements of the motor of the SEA by optimizing the stiffness of its spring. This study is inspired by the biomechanics of a human ankle joint, which stores elastic energy during the first phases of the walking process and releases the stored energy in the next gait phases to propel the human body forward. Power analysis and optimization procedure are conducted on complete SEA models with different complexity, including inertia, damping and stiffness, and with open loop and closed loop control strategies. Simulation results demonstrate that a reduction of 56.6% of the peak motor power can be achieved with the optimized spring stiffness.

In this paper, a novel constrained tendon-driven serpentine manipulator (CTSM) suited for minimally invasive surgery is presented. It comprises of a flexible backbone, a set of controlling tendons and a constraint. In the CTSM not only the curvature of the bending section can be controlled but also the length. Specifically, the curvature is controlled by the tendons, and the length is controlled by a constraint tube, which is translational and is concentric with the flexible backbone. The kinematic model of the CTSM is developed based on the piecewise constant curvature assumption. Analysis shows that by introducing the translational constraint both the workspace and dexterity of the manipulator are improved. The stiffer the constraint the larger the workspace expansion and the smaller the dexterity enhancement. A prototype is developed and the experimental results validate the design idea and analysis.

This paper proposes a new robust position control method for a novel series elastic actuator (SEA). It is a wellknown fact that SEAs provide many benefits in force control, e.g., lower reflected inertia and impedance, greater shock tolerance, safety, and so on. However, current SEA designs have a common performance limitation due to the compromise on the selection of spring stiffness. The performance of an SEA can be significantly improved by changing the stiffness of the spring; however, designing a variable-stiffness SEA is a quite challenging task. In this paper, a novel variable-stiffness SEA, which can relax the fundamental performance limitation of conventional SEAs, is proposed. It consists of torsional and linear springs, which have different compliances, in series. The soft and hard springs improve the performance when low and high force control applications are performed, respectively. Although SEAs have several advantages in force control, their position control problem is more complicated than the force control one. Moreover, using extra springs increases the number of vibration mode, which may significantly deteriorate the performance, in the position control problem of the SEA. In this paper, a new position control system, which improves the performance by increasing the robustness and suppressing the vibration, is proposed for a novel SEA. Experimental results are given to validate the proposal.

It is well-known that standard adaptive fuzzy control (AFC) can only guarantee uniformly ultimately bounded stability due to inherent fuzzy approximation errors (FAEs). This paper proves that standard AFC with proportional-derivative (PD) control can guarantee global asymptotic stabilization even in the presence of FAEs for a class of uncertain affine nonlinear systems. Variable-gain PD control is designed to globally stabilize the plant. An optimal FAE is shown to be bounded by the norm of the plant state vector multiplied by a globally invertible and nondecreasing function, which provides a pivotal property for stability analysis. Without discontinuous control compensation, the closed-loop system achieves global and partially asymptotic stability in the sense that all plant states converge to zero. Compared with previous adaptive approximation-based global/asymptotic stabilization approaches, the major advantage of our approach is that global stability and asymptotic stabilization are achieved concurrently by a much simpler control law. Illustrative examples have further verified the theoretical results.

High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot effectively relax the limitations of high-gain observers. This paper presents an output-feedback indirect ANC strategy under a nonseparation principle, where a hybrid estimation scheme that integrates an adaptive NN observer with state variable filters is proposed to estimate plant states. By applying a single Lyapunov function candidate to the entire system, it is proved that the closed-loop system achieves practical asymptotic stability under a relatively low observer gain dominated by controller parameters. Our approach can completely avoid peaking responses without control saturation while keeping favourable noise rejection ability. Simulation results have shown effectiveness and superiority of this approach.

Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human-robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human-robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.

This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis. The major difference of the proposed design from the traditional feedback adaptive approximation-based control (AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as NN inputs. Such a slight difference leads to several attractive properties, including the convenient NN design, the decrease of the number of NN inputs, and semiglobal asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach has two unique features: 1) all above attractive properties are achieved by a much simpler control scheme; 2) the bounds of plant uncertainties are not required to be known. Simulation results have verified the effectiveness and superiority of this approach.

This paper describes a novel approach to the problem of autonomous Robot Navigation in environments having less or no source of illumination. We have aimed at depth estimation and object recognition aspects, using the bio-inspired Dynamic Vision Sensor (DVS) asynchronous time-based image sensor (ATIS) silicon retina. Experiments were conducted in a dark environment using the ATIS camera, coupled with a simple point-like white LED light source mounted on the same. Switching the LED on for a fraction of time in the dark environment produced a diverging ripple of events in the ATIS. We show how this event response can be used to quantify the distance of the planar obstacle from the camera and also to characterize the object for use in object recognition. The ripple effect observed can be attributed to the high temporal resolution of the ATIS retina, the small rise time of the LED and the light intensity profile on the wall. In the initial sections of the paper, we have shown the theoretical basis for the phenomenon observed and then moved on to describe the proof of concept for depth estimation and object recognition. The algorithms can be used in robotic systems mounted with the ATIS and LED for real time depth perception and object recognition.

This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed-loop system. Based on a linearly parameterized raised-cosine radial basis function neural network, a key property of optimal approximation is exploited to facilitate stability analysis. It is proved that the closed-loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters. Compared with previous adaptive approximation-based semiglobal or asymptotic stabilization approaches, our approach not only significantly simplifies control design, but also relaxes constraint conditions on the plant. Two illustrative examples have been provided to verify the theoretical results.

We are developing an intelligent compact and modular knee-ankle-foot robot gait rehabilitation at outpatient and home settings. The robot is designed with a novel compact compliant force controllable actuator. We adopt a modular design for the knee and ankle joint so that the robot can assist patients with different conditions of gait impairments. The light-weight anthropomorphic structure designed based on biomechanical studies is built with advanced composite materials to achieve portability. A prototype of the robot has been built for testing. In this paper, we present the mechanical design of the robot with focus on the actuator and mechanism design and analysis, with testing results to demonstrate the performance of the actuator.

This paper presents a novel compliant differential (CD) Shape Memory Alloy (SMA) actuator with improved performance compared to traditional SMA actuators. This actuator is composed of two antagonistic SMA wires and a mechanical joint coupled with a torsion spring. The torsion spring is employed to reduce the total stiffness of SMA actuator and improve the range of motion. The antagonistic wires increase the response time as one wire can be heated up while the other wire is still in the cooling process. Dynamic model of this actuator was established for control design. Experimental results proved that this new actuator can provide larger output range of motion and faster response speed than traditional SMA actuators under the same conditions. Sine wave tracking with 0.05 Hz, 0.08 Hz and 0.1 Hz were performed and our results demonstrated that this compliant actuator has good tracking performance under simple PID control.

An omnidirectional platform with an active offset split caster (ASOC) is described and its ability to operate on non-ideal floors is studied. It is shown that all of its driven wheels of the platform will remain in contact with an uneven floor at all times, a condition necessary to maintain good traction and dead-reckoning capabilities. It is shown that planning algorithms developed for an ideally flat floor perform adequately for a realistic uneven floor. Furthermore, it is shown that the ASOC design consumes less power than other conventional wheel omnidirectional designs and is more suitable for heavier loads. Analytical and experimental results are presented.

Meeting the needs of the elderly presents important technical challenges. In this research, a system concept for a robotic aid to provide mobility assistance and monitoring for the elderly and its enabling technologies are being developed. The system, called PAMM (personal aid for mobility and monitoring), is intended to assist the elderly living independently or in senior assisted living facilities. It provides physical support and guidance, and it monitors the user's basic vital signs. An experimental test-bed used to evaluate the PAMM technology is described. This test-bed has a cane based configuration with a nonholonomic drive. Preliminary field trials at an Eldercare Facility are presented.