Precise kinematic modeling and control of soft continuum robots are challenged by their inherent deformability and complex interactions with the environment. This paper proposes an adaptive kinematic modeling framework based on a Deep Belief Network with Event-Driven Incremental Learning, designed for artificial intelligence-enabled control of soft robotic systems. The deep belief network is first pre-trained using simulated data to establish an initial kinematic model, requiring only approximately 600 real-world samples for deployment. An event-driven incremental learning mechanism is then introduced to adapt the model online. This mechanism is guided by a spike intensity metric, which evaluates prediction errors and selectively triggers either fine-tuning of network parameters or the integration of new radial basis function nodes to compensate for unmodeled kinematic effects and environmental disturbances. The adaptive kinematic model is embedded within a closed-loop controller to achieve accurate trajectory tracking. Experimental validation is conducted on a tendon-driven soft origami manipulator, covering trajectory tracking under varying payloads and deformation constraints, disturbance rejection, and teleoperation tasks. The proposed framework achieves sub-millimeter average trajectory tracking accuracy under confined conditions and outperforms baseline deep belief network models and piecewise constant curvature approaches. The results demonstrate that the proposed artificial intelligence-based adaptive kinematic modeling method provides a data-efficient and robust solution for precise control of soft continuum robots in applications such as medical robotics and flexible manufacturing.

Exoskeletons have been shown to effectively assist humans during steady locomotion. However, their effects on non-steady locomotion, characterized by nonlinear phase progression within a gait cycle, remain insufficiently explored, particularly across diverse activities. This work presents a shank angle-based control system that enables the exoskeleton to maintain real-time coordination with human gait, even under phase perturbations, while dynamically shaping assistance profiles to match the biological ankle moment patterns across walking, running, stair negotiation tasks. The control system consists of an assistance profile online generation method and a model-based feedforward control method. The assistance profile is formulated as a dual-Gaussian model with the shank angle as the independent variable. Leveraging only IMU measurements, the model parameters are updated online each stride to adapt to inter- and intra-individual biomechanical variability. The profile tracking control employs a human-exoskeleton kinematics and stiffness model as a feedforward component, reducing reliance on historical control data due to the lack of clear and consistent periodicity in non-steady locomotion. Three experiments were conducted using a lightweight soft exoskeleton with multiple subjects. The results validated the effectiveness of each individual method, demonstrated the robustness of the control system against gait perturbations across various activities, and revealed positive biomechanical and physiological responses of human users to the exoskeleton's mechanical assistance.

Soft artificial muscle actuators have gained attention in robotics for their remote control, fast response, and high compliance. However, replicating the intricate and efficient motions of natural muscles remains a challenge. Existing designs often lack the hierarchical and anisotropic properties of muscle sarcomeres, limiting their ability to achieve biomimetic movements. We developed a novel Biomimetic Magnetic Artificial Actuator (BMAA) inspired by muscle sarcomeres. Using a soft magnetic composite material arranged in a hierarchical structure, the actuator mimics the arrangement of actin and myosin filaments. External magnetic fields enable precise control of contraction and relaxation, emulating natural muscle motion. The driver can achieve the motion performance of muscle like motion characteristics, and its driving ability is verified by reptile experiment and elbow experiment. The actuator demonstrates significant deformation, fast response, and excellent controllability, enabling complex and precise movements. This research advances the development of biomimetic soft actuators, offering potential applications in soft robotics, biomedical devices, and artificial muscles, and paving the way for more versatile and intelligent machines.

It remains a formidable challenge to accurately recognize motion intentions of patients thus to control hand exoskeletons according to their volition. Current methods primarily focus on recognition of limited patient’s motion intentions, with the purpose of controlling preconfigured gestures of a hand exoskeleton for grasping objects. These methods exhibit a marked shortfall when encountering scenarios that are unexpected or not designed in advance, such as non-preprogrammed hand movements and object manipulation tasks. To tackle this issue, large language model (LLM) and speech recognition technology are employed in this study to allow the patient to control a hand exoskeleton at will. In particular, two LLMs are tailored to formulate codes of either generating non-preprogrammed gestures or dealing with unencountered objects. Additionally, an incremental learning framework is proposed to enable patients to perform both predefined and non-predefined operation tasks by integrating a natural language parser with the two LLM-based learners. The natural language parser can directly control the hand exoskeleton to perform predefined operations tasks from prestored command set, while the LLM-based learners can incrementally expand the control command set so as to enhance adaptability of the hand exoskeleton to complex activities over daily use. This study is a pioneering work in the field of hand exoskeletons, which will revolutionize the way to control hand exoskeletons. Furthermore, the proposed framework can be easily generalized to any other robots by modifying the prompt of customized LLMs, which provides a new idea to achieve autonomous learning in robotics.Note to Practitioners—The motivation of this article is to tackle the challenge of intention recognition for performing activities of daily living (ADLs) by stroke patients using a multi-degree of freedom hand exoskeleton. Existing methods for intention recognition so far can only be used for several tasks that are predefined in advance, thus none of them allow patients to control the hand exoskeleton completely at will. To surpass this limitation, an LLM-enabled incremental learning framework that integrates a hand exoskeleton controller with Large Language Model (LLM) is proposed and validated in this study. The framework offers patients an intuitive interface via voice interaction and enables patients to perform not only predefined operation tasks by the hand exoskeleton controller but also non-predefined ones that can be learned from the LLM. As a result, the hand exoskeleton controller continues to learn from the LLM, therefore is gradually able to perform all tasks in daily life. This pioneering study paves a new way in building patient-controlled hand exoskeletons with autonomous intelligence that can deal with non-predefined operation tasks in unstructured environments.

This paper introduces the Adaptive Residual Decision-Control Synthesis (ARDCS) framework, a hierarchical control architecture that synergizes a model-based Linear Quadratic Regulator (LQR) with an adaptive Dynamic Proximal Policy Optimization (DPPO) agent for autonomous bicycle control. ARDCS is designed to master the bicycle’s complex nonlinear dynamics and adapt to environmental uncertainties by leveraging the stability of traditional control with the flexibility of reinforcement learning. A key innovation is a momentum-enhanced dynamic entropy adjustment mechanism within DPPO, which optimizes the exploration-exploitation trade-off for more stable and efficient learning. Comprehensive experiments on balancing and multi-target navigation tasks demonstrate that ARDCS consistently and significantly outperforms both conventional control methods and pure reinforcement learning strategies across varying levels of difficulty. The framework achieves enhanced adaptability and robust stability without relying on auxiliary mechanical stabilizers, offering a potent and generalizable solution for the control of under-actuated systems.

This study focuses on enhancing the precision and efficacy of pedicle screw placement in spinal surgeries, particularly for patients with osteoporosis. It emphasizes the importance of accurate screw positioning to maximize pullout strength and biomechanical efficacy. The research highlights the relationship between CT values, Bone Mineral Density (BMD), and the Young’s modulus of bone tissue, suggesting that higher CT values, indicative of denser bone, lead to stronger mechanical properties. This understanding is crucial for assessing bone health, especially in osteoporosis and fracture risk analysis. The study introduces an innovative automated planning method for pedicle screw insertion in spinal vertebrae, beneficial for osteoporotic patients. This method uses PointNet++ combined with a Siamese network for semi-supervised segmentation of vertebrae in CT images, converting them into point clouds for individualized planning. The approach aims to improve accuracy and efficiency in pedicle screw placement, especially in complex or osteoporotic vertebrae. The method was validated using osteoporotic in vitro models, demonstrating its potential effectiveness for surgeons facing challenges in pedicle screw placement in osteoporotic patients. The complete workflow begins with semi-supervised vertebra segmentation using a Siamese PointNet++ architecture. The extracted vertebra point cloud is then aligned with a predefined pedicle screw model via an adaptively tuned Super4PCS registration algorithm to generate patient-specific trajectories. The paper also reviews traditional surgical path planning, which relied on surgeons’ experience and intuition, and the shift towards Computer-Aided Design (CAD) and Virtual Reality (VR) technologies for pre-operative planning. Despite these advancements, challenges remain in accurately simulating tissue properties and managing physiological variations. The study proposes a bifurcated approach to automated trajectory planning: segmenting individual vertebrae and planning pedicle screw implantation for each segmented vertebra. The method involves converting CT scans into 3D point clouds, using PointNet++ and the Siamese method for vertebra segmentation, and registering the pedicle screw point cloud with the vertebra model for precise surgical planning. The study demonstrates the method’s feasibility through clinical data from Shengjing Hospital, showing high applicability and consistency in surgical path planning, particularly in the lumbar and lower thoracic regions. The planning results were consistent with surgeons’ experience, indicating the algorithm’s adaptability and stability.

This paper investigates the prescribed performance formation tracking control problem for nonlinear multiagent systems (MASs) under state constraints. Compared with the existing results using predefined-time distributed observers, an observation error compensation term is designed to prevent the tracking error caused by the estimation error from violating the prescribed performance. Different from the existing related literature, this paper introduces a novel approach by combining a state-dependent transformation method with a time-varying constraint-based prescribed performance control (PPC) method. This approach effectively eliminates the feasibility conditions of state constraints and the initial condition dependence restriction of PPC. Moreover, by designing an appropriate PPC-related time-vary function, the proposed method can accurately map the preset control performance before and after the transformation process. Therefore, the loss of state feasible sets in the existing relevant results can be tackled. The simulation example verifies the feasibility of the control method. Note to Practitioners—This paper studies the prescribed performance formation tracking control problem for nonlinear MASs, which has application value in autonomous driving, smart factories, environmental exploration, unmanned delivery and other fields. By using a state-dependent transformation method and a time-varying function, a PPC strategy, under state constraints, is devised for MASs formation. Moreover, a compensation term is designed to handle the tracking errors caused by the observation errors before the predefined observation time.

While rehabilitation exoskeletons have been extensively studied, systematic design principles for effectively addressing heterogeneous bilateral locomotion in hemiplegia patients are poorly understood. In this article, a multijoint lower exoskeleton driven by series elastic actuators (SEAs) is developed, and the design philosophy of rehabilitation robots for hemiplegia patients is systematically explored. The exoskeleton has six powered joints for both lower limbs in a hip–knee–ankle configuration, and each joint incorporates a custom, lightweight SEA module. A unified interaction-oriented control framework is designed for exoskeleton-assisted walking, including gait generation, task scheduling, and advanced joint-level control. The closed-loop design provides methodical solutions to address hemiplegia rehabilitation needs and provides walking assistance for bilateral lower limbs. Moreover, a multitemplate gait generation approach is proposed to address the altered kinematics induced by exoskeleton-assisted walking and enhance the exoskeleton's adaptability to patient-specific kinematic variations in an iterative manner. Experiments are conducted with both healthy individuals and hemiplegia patients to verify the effectiveness of the exoskeleton system. The clinical outcomes demonstrate that the exoskeleton can achieve mechanical transparency, facilitate movement, and enable coordinated interjoint locomotion for bilateral gait assistance.

Rehabilitation exoskeletons have been demonstrated to benefit mobility-limited patients; however, accessibility is hindered by several challenges, and interaction evaluation of coupled human–exoskeleton systems remains critically understudied. In this pioneering study“, assistive normality (AN)” is introduced to characterize the cross-stage spatiotemporal features of exoskeleton-assisted gait in hemiplegic patients, and a multidimensional, low-data-cost metric framework is developed to quantify AN. Three subdivided metrics, including gait restoration (GR), phase-deviation weighting (PDW) and multijoint coordination (MJC), are proposed to assess coupled system interaction behavior across different intervention stages during rehabilitation. A homologous difference evaluation paradigm (HDEP) is introduced to capture pathological differences between healthy and hemiplegic subjects on the basis of the reusability of experimental data, providing an approach for the nominal assistance calibration and assessment of specific exoskeleton devices. In a pilot study with eight healthy individuals and nine hemiplegic patients, between-group metric differences were analyzed to determine the calibrated AN of an exoskeleton. The results provide calibration references for quantitative metrics and demonstrate the ability of framework to characterize the temporal dynamics of human–exoskeleton interactions. The proposed AN framework offers a generalizable approach to assistive robotics, potentially enhancing evaluation of human-robot interaction and advancing clinical rehabilitation applications.

Soft manipulators offer the advantages of safety and adaptability. However, due to insufficient stiffness and single motion mode limitations, existing soft manipulators usually exhibit low load capacity and small working space. To address this problem, we propose a novel soft hybrid-driven manipulator with continuous stiffness control capability and multiple motion patterns (omnidirectional bending and extension). Furthermore, we develop kinematic and stiffness models based on the constant curvature assumption. The soft robot consists of a soft bellows actuator and inextensible rigid skeletons, which exhibit a high extension ratio and low drive pressure. With the antagonistic actuation of tendon-pulling and air-pushing, the robot can achieve independent control over stiffness and position in three-dimensional space. The performance associated with the designed soft hybrid-driven manipulator is experimentally verified. The robot can achieve an elongation of 198% and a maximum bending angle of up to 240°. The robot can also increase stiffness by increasing internal air pressure to resist deformation caused by external loads. Additionally, tracking experiments with various trajectories in space verify the accuracy of the kinematic model, which indicates that the soft manipulator can stabilize motion within a broad workspace.

This paper investigates the fully distributed secure state estimation problem of nonlinear multi-agent systems (MASs) against denial-of-service (DoS) attacks. The design of state observer depends on the relative measurement outputs between neighboring agents rather than the absolute ones of agents themselves. To cooperatively estimate system states with the influence of DoS attacks in a fully distributed scenario, an edge-pinning-based synchronous update strategy for coupling weights is proposed. The purpose of adopting edge pinning method is to adjust partial coupling weights between neighboring agents instead of all ones, which can effectively reduce estimation complexity and save computing resources, especially for large-scale MASs. Further, an edge-pinning-based asynchronous update strategy for coupling weights is designed to meet different actual demands. Then, the security state estimation schemes under synchronous and asynchronous update strategies are proposed without utilizing any global information, such as the Laplacian matrix of communication graph or its smallest nonzero eigenvalue, and so on. Finally, a simulation example is provided to verify the theoretical results.Note to Practitioners—This paper addresses the distributed secure state estimation problem for nonlinear MASs, which plays an important role in unmanned aerial vehicle (UAV) formation, intelligent transport systems, smart grids, and so on. The absolute measurement outputs of agents themselves are usually unavailable in practical situations, so the relative measurement outputs between neighboring agents are used to design observers. Moreover, to overcome the difficulty that global information is unavailable in industrial applications with DoS attacks, the edge-pining-based synchronous and asynchronous update strategies for coupling weights are proposed in a fully distributed scenario.

This article studies the adaptive fault-tolerant consensus tracking control problem for a class of stochastic nonlinear high-order MASs under stochastic FDI attacks. Based on the FLSs and backstepping design technique, a novel adaptive fuzzy fault-tolerant consensus tracking control strategy is proposed to guarantee that the closed-loop stochastic high-order MASs are semi-globally stable in probability via stochastic Lyapunov stability theory and It$\hat{o}$lemma. In order to relieve the communication pressure of MASs, a novel high-order dynamic event-triggered mechanism (DETM) based on user-set parameters is developed. Compared with the traditional event-triggered mechanism, this DETM has larger triggering time intervals, effectively reduces communication frequency, and has greater flexibility in balancing system performance and communication bandwidth resource constraints. Finally, the actual simulation and comparative simulation are given to verify the effectiveness and superiority of the proposed scheme.

This article presents a distributed prescribed performance formation tracking control scheme for nonlinear multiagent systems with the consideration of collision avoidance. Specifically, a novel prescribed performance control (PPC) method is designed. Different from the existing results, the predefined tracking error convergence region and the predefined settling time can be set without constraints. Moreover, the PPC method can remove the initial condition-dependence restriction of tracking errors. In addition, a predefined-time distributed observer is produced to estimate the information of the reference signal. Contrary to the existing related literature, the estimation errors can asymptotically converge to zero within a predefined time, even in the case of unknown disturbances. Combining a Lyapunov–Barrier function and the PPC method, the proposed control protocol enables agents to avoid obstacles and ensures that the formation tracking errors reach the predefined convergence region within a predefined time. Finally, a simulation demonstrates the validity of the algorithm.

Sub-manifold learning has been widely used to project high-dimensional data into a low-dimensional manifold, preserving the structure of the original data as much as possible. Existing sub-manifold learning methods either learn an embedding manifold that may have different geometric properties as the original data space, or learn a sub-manifold without considering the nonlinear structure of the data. In this paper, we learn a sub-manifold of the original data based on learned prototypes which represent prior knowledge about the intrinsic features of the data. This allows to incorporate the prior knowledge existing in the prototypes to find the suitable sub-manifold. The sub-manifold and the prototypes are jointly learned in a unified cost function via the gradient descent algorithm. The learned prototypes are obtained in the original high-dimensional space and subsequently used to learn a projection matrix to map the high-dimensional data into a lower-dimensional subspace with better separability. The prototypes are relearned in the projected subspace. The relearned low-dimensional prototypes are then working as prior knowledge to induce the learning of a better projection matrix, leading to a better subspace. The proposed subspace learning is realized for data points living in the Riemannian space of symmetric positive definite (SPD) matrices via the generalized learning Riemannian space quantization (GLRSQ) method. Experiments on both synthetic and real-world data sets show the effectiveness of the proposed dimension reduction scheme.

When applying exoskeletons for walking assistance, one important consideration is to ensure that the users retain full control over the exoskeleton-provided assistance, which is quite limited in existing exoskeletons due to the absence of a suitable control system. In this article, a time-independent exoskeleton control system is developed based on a novel assistance profile generation method and an iterative force control method to enable continuous assistance adjustment. The assistance profile is formulated as a Gaussian function with a human state variable and can be updated online to adapt to different users. The proposed profile continuously self-adjusts along the movement of the user's leg, especially when users change their walking patterns. The proposed control system iteratively compensates for the force control lag and amplitude attenuation to enable precise tracking of the assistance profile during natural human walking. Experiments have been conducted using a soft exoskeleton on subjects with and without prior experience using an exoskeleton. The experimental results have shown the effectiveness of the proposed control system compared with a common time-dependent control system.

In order to meet the mobility and physical activity needs of people with impaired limbs function, a novel limbs-free variable structure wheelchair system controled by face-computer interface (FCI) was developed in this study. FCI used facial electromyography (fEMG) as a human intention recognition method from 6 facial movements, and the accuracy of intent recognition reached 97.6% under a series of offline optimization including channel optimization based on the Hilbert transform to obtain the envelope of fEMG, features optimization, and channel-independent model optimization. A collection of finite state machines (FSM) was used to control the movement and structural changes of the wheelchair. A shared control strategy called “ Keep Action after Take Over (KAaTO) “ that can reduce user fatigue while increasing safety was used in long-distance movement control of wheelchair. To test the performance of the system, in the braking distance test experiment, the result of 0.429m under KAaTO was better than the EMG-based discrete command control and speech command control method. Finally, an outdoor long-distance control pilot experiment proved the superior performance of the developed system.

Many existing works represent signals by covariance matrices and then develop learning methods on the Riemannian symmetric positive-definite (SPD) manifold to deal with such data. However, they summarize each instance with a single covariance matrix, omitting some potential important information, such as the time evolution of the correlation in signals. In this paper, we represent each instance by a sequence of covariance matrices and develop a novel dynamic generalized learning Riemannian space quantization (DGLRSQ) method to deal with such data representations. The proposed DGLRSQ method incorporates short-term memory mechanism in generalized learning Riemannian space quantization (GLRSQ), which is an extension of Euclidean generalized learning vector quantization to deal with SPD matrix-valued data. The proposed method can capture the temporal evolution of the correlation in signals and thus provides better performance to its the counterpart – GLRSQ, which treats each instance as a signal covariance matrix. Empirical investigations on synthetic data and motor imagery EEG data show the superior performance of the proposed method.

Soft exoskeletons have demonstrated the potential to save energy, but their efficiency is sensitive to variations in human gait cadence. This work aims to develop adaptive gait state detection and iterative force control methods for a soft exoskeleton to reduce human walking metabolic cost consistently, while the user may change walking cadence. The proposed approach is motivated by the rhythmicity of gait and applies an iterative learning concept to enhance the exoskeleton’s adaptability to varying walking conditions. The gait state detection method proposed for the designed exoskeleton combines two feature extraction algorithms, which can learn from the present and past body kinematic data, to provide accurate user gait state detection. Based on the state, the proposed force control method iteratively adjusts the commands to keep track of the desired profile. Experiments have been conducted on healthy subjects walking with varying cadence using the soft exoskeleton. Promising results were presented in separate validation tests. Moreover, metabolic costs of subjects walking under one unpowered and two powered conditions, where the assistance profiles were produced by classical methods and the proposed methods, showed that the proposed methods can effectively improve the exoskeleton’s ability to save human energy of walking with varying cadence.Note to Practitioners—Lower limb exoskeletons have demonstrated the potential to save human energy in medical and industrial applications. The main purpose of this work is to solve the exoskeleton assistance efficiency loss problem for users walking with changing cadence. Constant cadence is unlikely maintained during natural human walking. Few existing exoskeletons could retain high efficiency under user cadence changes, limited by their control system capability. This work presents a new cable-driven cadence-insensitive soft exoskeleton, which is purposely designed with two adaptive methods to enable the device to offer consistent benefit to users walking with varying cadence. The proposed methods are inspired by the rhythmicity of human gait and can be iteratively reconfigured to perform accurate human gait state detection and assistive force tracking. The proposed methods have the potential to be integrated into other human-oriented robots to improve their adaptability. This work can greatly enhance the possibility of using the walking assist robotic devices in more practical applications.

This article studies the leader-following consensus of multiagent systems (MASs) with completely event-triggered mechanisms (CETMs) and directed switching topologies. When most event-triggered schemes in MASs only reduce each agent's data transmissions to neighbor agents, CETMs further reduce data transmissions to actuators with one triggered function in each agent. To guarantee the switching links utilized by CETMs contain a directed spanning tree at any time, triggered decisions are improved to include the instants at which each agent's output links or leader link change. Furthermore, a less conservative method based on matrix inequalities is combined to minimize the allowable average dwell time (ADT) of switching topologies under the design conditions of CETMs. Based on multiple Lyapunov functions (MLFs), the sufficient conditions for controller gains that guarantee the leader-following consensus of MASs are proposed when the ADT of switching topologies is larger than the allowable value. Finally, an example of autonomous underwater vehicles (AUVs) is given to illustrate the effectiveness of CETMs.

It has been clinically proven that exoskeletons are effective self-training rehabilitation or daily living assistance devices for patients with hand dysfunctions. However, exoskeleton-assisted hand exercises with high degrees-of-freedom are considered as challenging tasks because the digit space, especially the thumb, cannot accommodate enough actuators. In this article, we report a tendon-driven soft hand exoskeleton with a hybrid configuration for thumb actuation. The soft hand exoskeleton system uses the least number of actuators to realize full degrees-of-freedom actuation for all digits. It is tested on a stroke patient with hemiplegia and a healthy subject. The experimental results show that the hand exoskeleton could assist the stroke patient to accomplish various training tasks, such as thumb encircling, grasping, pinching, releasing, and writing. It was found that digit trajectories and joint angle changes of the stroke patient were close to those of the healthy subject. Especially, the range of motion of the stroke patient shows significant improvement with the hand exoskeleton assistance compared to that without the hand exoskeleton assistance. The research in this article paves the way to develop fully actuated soft hand exoskeleton that can be eventually integrated with an electroencephalogram or electromyography for self-training rehabilitation or daily living assistance.

Human locomotor activity (LA) recognition is important in the control of exoskeletons and prostheses and in patient monitoring. This article presents a practical recognition approach that can classify level walking, stair ascent, and stair descent activities across different subjects and diverse gait patterns. The thigh angle is measured and utilized in this method to construct a phase curve in an activity-specific coordinate frame during a stride. The LA is recognized by matching the curvature of its phase curve to the expected one. The factors affecting the adaptability of the proposed method to gait variations are analyzed and compensated for. The proposed method is evaluated with eight subjects who are asked to perform the three types of activity at two different cadences: 70 steps/min and 110 steps/min. Experimental results show that the proposed classifier outperforms an existing phase variable based classifier in all validation experiments and a${\boldsymbol{k}}$-nearest neighbor classifier when using nonsubject-specific training data, indicating that the proposed method has superior adaptability to changes in human and in strides. Moreover, the feature used in the proposed method has demonstrated the potential in quantitatively indicating the extent of neuromotor impairments of patients.

In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about -0.017±0.13 radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% ( ) average accuracy of results obtained using nonadaptive models, and 60.99% ( ) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects' re-training burden of sEMG-based rehabilitation systems.

Rotorcrafts, with satisfactory maneuver performance and ability under complex terrains unreachable for ground robots, are playing important roles for goods transportation. In this article, we focus on the control of the cable-suspended transportation way due to its lower costs and more agility of the rotorcraft's rotational motion. Compared with traditional crane systems and single rotorcrafts without loads, the aerial transportation system presents “double” underactuated property, stronger system nonlinearity, and more complex dynamic coupling, which are huge challenges for control schemes design. Meanwhile, aerial transportation usually suffers from external disturbances and uncertainties presented with aerodynamic damping coefficients and rope length. Additionally, overshoots of the rotorcraft's position are potential threats for flight safety, especially in confined and complex environments. To address these problems, a novel adaptive control scheme is designed, which ensures effective rotorcraft positioning and payload swing suppression with restricted overshoot amplitudes. Asymptotic results are obtained with rigorous theoretical derivations provided by the Lyapunov-based stability analysis and LaSalle's invariance theorem. Real-time experiments are performed to validate the effectiveness of the proposed control scheme even in the presence of external disturbances. To the best of our knowledge, this is the first method designed for aerial transportation systems which achieves simultaneous rotorcraft positioning and swing suppression, together with insurance for overshoot restriction even in the presence of parametric uncertainties.

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.

Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain–computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia.

sEMG recognition has been used extensively in prosthetic device control, human-assisting manipulators and sign language recognition, etc. However, the sEMG recognition model, trained with one subject's sEMG data, is not applicable to the other subjects, which hinders the practical application of myoelectric interfaces immensely. In this paper, a sEMG recognition method which is applicable to multi-users is proposed. Firstly, single channel sEMG is decomposed into 30 MUAPTs, which includes four steps: two-order differential filter, threshold calculation, spike detection and hierarchical clustering. Secondly, the MUAPTs are updated with the templates orthogonalization; and Deep Boltzman Machine is employed to classify the MUAPTs into five classes corresponding to the predefined five gestures. Six participants participated in this experiment to validate the effectiveness of the proposed method. Results indicated that this method can achieve a mean accuracy of 81.5%.

A multifunctional myoelectric prosthetic hand is a perfect gift for an upper-limb amputee, however, the myoelectric control for a prosthetic hand is not so good now. Here, the paper presents a comparative study on electromyography (EMG) pattern recognition based on PCA and LDA for an anthropomorphic robotic hand. Four channels of surface EMG (sEMG) signals were recorded from the subject's forearm. Time-domain analysis, frequency-domain analysis, wavelet transform analysis, nonlinear entropy analysis and fractal analysis were done and fourteen kinds of features were extracted from sEMG signals. The features were divided into four groups, and the performances of the four groups were compared and analyzed. In the feature projection stage, three schemes were proposed and their performances were compared with each other. The first one only used the principal component analysis (PCA) for dimension reduction. And the second one only used the linear discriminant analysis (LDA) for dimension reduction. The third one used PCA for the first step of dimensionality reduction, and then used LDA for the next step of dimensionality reduction. In the classification stage, minimum distance classifier (MDC) was employed for identifying nine kinds of hand/wrist motions in the projected space. Comparative experiments of four groups of features and three projection schemes were done and evaluated. The online experiment of real-time myoelectric control for an anthropomorphic robotic hand was done as well.

Estimation of the joint angle from the surface electromyography (sEMG) is a quite complex task due to the complicated relationship between the kinematical variables and the raw sEMG. In this paper, we build a sEMG-to-motion model with the artificial neural network (ANN). EMG features, including Integral of absolute value (IAV), Zero crossing (ZC), Auto-regression coefficients (ARC), Median frequency (MDF), are extracted as the input of the ANN, and the output of the ANN is the operator's elbow joint angle and the wrist motion. In addition, a 3D upper extremity model, which is built in SolidWorks and then transformed into MATLAB, will imitate the operator's motion simultaneously with the estimations of the ANN. Thus, we accomplish a virtual reality system to realize the real-time simulation and validate the effectiveness of the sEMG-to-motion model. Experiment results show that the system achieves well in model accuracy, hardware compatibility and real time performance with a small mean square error of 1.921 degrees.

Flexible needle with bevel tip offers greater mobility for puncture surgery. This would expand the scope of the puncture surgery. However, motion planning for flexible needle is still a challenge due to its non-holonomic property and the complicated interactions with soft tissues. In this paper, a multilayer tissue model is constructed to simulate human tissue, and a dynamic programming is employed to plan the motion of flexible needle in the multilayer environment. In order to improve the security of the puncture process, the obstacles are fuzzed up. Then, an optimal algorithm is developed to determine a more suitable puncture angle. In addition, to deal with more complex environment, we develop a reverse algorithm to confirm the entry point in line with the target. Finally, we take some simulations to verify the proposed algorithms, and analyze the results.

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state space model is developed to estimate continuous joint angular displacement and velocity, demonstrated by elbow flexion/ extension. The model combines the Hill-based muscle model with the forward dynamics of joint movement, in which kinematic variables are expressed as a function of neural activation levels. EMG features including integral of absolute value and waveform length are then extracted, and two quadratic equations which associate the kinematic variables with EMG features are constructed to represent the measurement equation. The proposed model are verified by extensively experiments, where the angular movements of human elbow joint are estimated only using the EMG signals, and the estimations are compared with the IMU measurements to validate the accuracy. As a demonstration, a robotic arm is commanded to follow the human elbow movement estimated by the proposed model, which shows the possibility of EMG-based robotic assisted rehabilitation.

In this paper, we deal with the yaw control problem of a small-scale helicopter mounted on an experimental platform. The yaw dynamics of helicopter involve input nonlinearity, time-varying parameters and the couplings between main and tail rotor. An attractive control strategy that combines neural networks with traditional adaptive controls has been successfully used for yaw control with input nonlinearities. In contrast to conventional adaptation law, the sliding condition is taken as the objective function instead of the error function used in MIT rule. From the concept of the sliding mode control, the adaptive controller guarantees the stability of the closed-loop system and convergence of the output tracking error to a desired bound, even if the model parameters are unknown or in the presence of disturbance. The simulation results are further compared with those obtained by normal PID control to demonstrate the improvements of the proposed algorithm

This paper presents a new robust controller design approach to the yaw control of a small-scale helicopter mounted on an experimental platform. The yaw dynamic system is linearized into a linear system, which is modelled by an affine uncertainty model. We proposed a novel robust Hinfinfeedback controller with adaptive mechanisms for the linear system with guaranteed control performances. The feedback gains are obtained by the solutions of a series of linear matrix inequalities (LMIs). The design approach reduces conservatism inherent in robust control with a fixed gain controller and improves performances in time-response. Numerical simulations illustrate the theoretical results