Hardware image signal processing (ISP) transforms RAW inputs into high-quality RGB images through a series of processing modules, each with numerous tunable parameters. Traditionally, these parameters are manually tuned by imaging experts, a time-consuming and subjective process. Recent deep learning approaches predict ISP parameters, but often treat the process as a black box and overlook the intrinsic relationships among ISP modules. To address these fundamental issues, we introduce a novel ISP parameter optimization model based on single-agent reinforcement learning (RL) (i.e., SARL-ISP), formulating the hardware ISP parameter tuning as a sequential optimization problem. During the optimization process, the agent updates ISP parameter tuning strategies for different tasks through interaction with the environment. In order to explore the influence of the sequential structure of hardware ISP modules and the coupling relationships among ISP parameters on the tuning process, we further propose a sequential ISP framework based on collaborative multi-agent RL (i.e., MARL-ISP). Specifically, the serialized parameter tuning module (SPTM) realistically simulates the process of manual prediction and module pipeline. Additionally, the feature selection module (FSM) facilitates the transmission and fusion of agent features, thereby selecting more appropriate feature inputs for downstream tasks. Extensive experiments across various tasks (e.g., object detection, instance segmentation) validate the effectiveness and efficiency of our models. Even with minimal training data, our models also outperform current state-of-the-art methods in both quantitative metrics and qualitative evaluations.

This study presents an innovative hand exoskeleton based on a linkage-transmission mechanism, aimed at supporting individuals with motor impairments in executing grasping activities essential for daily life. An underactuated mechanism leveraging redundant degrees of freedom is proposed, enabling the exoskeleton to adaptively grasp objects of varying shapes and sizes. The mechanical design ensures kinematic compatibility between the finger joints and the exoskeleton by establishing appropriate constrained kinematic chains. Its kinematics and quasi-statics are systematically analyzed to optimize the mechanical design. To evaluate the functional performance of the assistive hand exoskeleton, a systematic assessment framework is developed, incorporating three dimensions: subjective perception, kinesiology, and physiological response. Two experimental paradigms were conducted using the hand exoskeleton prototype with six healthy participants. Six assessment metrics (motion transparency, grip similarity, muscle activation, muscle fatigue, the System Usability Scale (SUS), and the Rating of Perceived Exertion (RPE) scale) were employed to assess the exoskeleton’s functionality. Experimental results demonstrate that the proposed exoskeleton effectively aids users in grasping various objects while significantly reducing physical exertion and fatigue.

This study introduces iProDMP, a novel framework that integrates human behavioral preferences into exoskeleton learning through unified probabilistic movement modeling. The framework addresses three critical limitations: Dynamic movement primitives (DMPs)’ inability to represent preference uncertainty, inadequate trajectory generation beyond observation range in probabilistic movement primitives (ProMPs), and unreliable via-point modulation in hybrid approaches. Our solution features three innovations: First, we establish dynamic-probabilistic consistency conditions for unified DMP-ProMP frameworks, which enable stochastic modeling of human preferences while preserving attractor stability. Second, a novel scaling method decouples shape modulation from model hyperparameters, enabling flexible motion adaptation. Third, consistency-guaranteed expectation-maximization resolves parameter optimization within the unified framework. Experiments on lifting trajectory imitation demonstrate strong extrapolation beyond demonstration distributions, particularly for via-points outside training data, thereby validating adaptability to variable task conditions. In hip exoskeleton-assisted lifting tasks, our approach achieves a 28% improvement in assistance efficiency over conventional implementations.

This paper proposes a Bidirectional Mamba-based selective state space model (SSM) for continuously predicting human motion intention using multisource information fusion of surface electromyogram (sEMG) and mechanomyography (MMG). In the proposed prediction model, the bidirectional canning mechanism and the dynamic selection mechanism make the prediction model have global context modeling capability and optimal allocation of computing resources, and the SSM makes the prediction model maintain linear computational complexity. By integrating the complementary features of sEMG and MMG through multimodal signal fusion, the deep information of motion intention is deeply explored, significantly enhancing the model’s intention prediction ability. The effectiveness and superiority of the proposed Bidirectional Mamba-based human motion prediction model are validated in comparison with the information fusion models based on recurrent neural network (RNN), long short-term memory network (LSTM), and temporal convolutional network (TCN).

Soft robotic devices, known for their high compliance, are increasingly being used in assistance and rehabilitation. However, the limited force output of soft actuators has hindered their broader adoption. In this study, a lobster-tail-inspired high-force-output soft pneumatic bending actuator (SPBA) is developed, featuring a soft deformable body and a rigid kirigami limiting shell. The SPBA, with a radius of 10 mm, can generate forces of approximately 22 N at an internal pressure of 0.1 MPa and 36.43 N at 0.16 MPa. An analytical model based on the Euler–Bernoulli beam theory, incorporating a hyperelastic material model, has been constructed to predict the deformation and force of the actuated SPBA. This model demonstrates good agreement with simulated and experimental results. For assistance, a soft robotic gripper with four SPBAs can lift a weight of 5.38 kg at 0.26 MPa. For rehabilitation, an SPBA-based hand exoskeleton has been developed, demonstrating significant effectiveness in mitigating hand spasticity following strokes. This study introduces a novel SPBA design with promising potential for future applications in grasping, assistance, and rehabilitation.

Current hand exoskeleton interaction methods primarily focus on recognizing a limited range of hand motion intentions and rely on pre-programmed control to execute predefined commands. However, these approaches face significant limitations when confronted with unanticipated or non-predefined scenarios, such as performing various gestures or grasping different objects. To address this challenge, this paper proposes an embodied interaction control (EIC) framework for hand exoskeletons based on a multimodal large language model (MLLM). First, an embodied interaction method leveraging multi-modal fusion of speech and image information is developed, enabling more intuitive, hands-free, accurate, and robust human-robot interaction. By utilizing multi-modal data, the MLLM infers the user’s hand motion intentions and generates corresponding motion plans for the exoskeleton. The underlying control strategy is then used to execute the motion planning. Notably, leveraging the advanced reasoning and code-generation capabilities of MLLMs, the framework can generate undefined gestures and grasping actions. Finally, experimental results validate the effectiveness and generalizability of the EIC framework.

Point-to-point and periodic motions are ubiquitous in the world of robotics. To master these motions, Autonomous Dynamic System (ADS) based algorithms are fundamental in the domain of Learning from Demonstration (LfD). However, these algorithms face the significant challenge of balancing precision in learning with the maintenance of system stability. This paper addresses this challenge by presenting a novel ADS algorithm that leverages neural network technology. The proposed algorithm is designed to distill essential knowledge from demonstration data, ensuring stability during the learning of both point-to-point and periodic motions. For point-to-point motions, a neural Lyapunov function is proposed to align with the provided demonstrations. In the case of periodic motions, the neural Lyapunov function is used with the transversal contraction to ensure that all generated motions converge to a stable limit cycle. The model utilizes a streamlined neural network architecture, adept at achieving dual objectives: optimizing learning accuracy while maintaining global stability. To thoroughly assess the efficacy of the proposed algorithm, rigorous evaluations are conducted using the LASA dataset and a massage robot task. The assessments were complemented by empirical validation, providing evidence of the algorithm’s performance.

Compared with traditional underwater vehicles, robotic fish have been receiving increasing attention in recent years due to their excellent maneuverability. However, the characteristics of fishlike undulatory motions and complex underwater working environment have posed significant challenges to robotic fish speed measurement, limiting their autonomy. To overcome these challenges, an artificial lateral line sensor (ALLS) was developed, drawing inspiration from the tactile system of fish. It captured the real-time speed of robotic fish through assessing the deformation of the stressed component under laminar flow impact. To mitigate turbulence disturbances near the ALLS, three flow control components, fairing, flow conditioner, and flow collector, were proposed to attenuate turbulence noise under the viscous effect. Furthermore, a physics-informed calibration method was presented to establish the nonlinear model of ALLS. Specifically, a physical model embedding algorithm based on data resampling was used to mitigate the risk of overfitting by the multilayer perceptron, considering the influence of turbulence disturbance and fishlike undulatory noise. Compared with the classical calibration method based on physical model fitting, the calibration method proposed in this paper reduced the error by 36.0%. Our ALLS’s final mean absolute error was 0.016 m/s with a linearity (R2) of 0.956. The experimental results indicated that the significant changes in the motion state of robotic fish reduced the accuracy of ALLS. The fusion with other sensors is expected to enhance the robustness of ALLS in the future. Note to Practitioners—The motivation of this paper is to design an artificial lateral line sensor based on surface flow field detection and turbulence noise suppression, providing a small-sized and high-precision solution to the speed measurement problem of bionic robotic fish. Most existing ALLS research focused on developing new types of sensors based on different measurement principles, without suppressing the noise caused by fishlike motions, and most experiments were conducted in environments with excessive controls rather than free-swimming robotic fish. To this end, we developed an ALLS based on deformation measurement and proposed three flow control components to make the measured flow more stable. Furthermore, a physics-informed overfitting suppression method was used for the calibration task of the ALLS. A series of simulations and experiments demonstrated that the proposed turbulence noise suppression and calibration method were practical and effective. Hopefully, our methods can provide theoretical and technical guidance to marine engineers for underwater vehicle speed measurement and flow sensing. The recommended flow control component is applicable for conditioning surface fluids in pneumatic control systems. Furthermore, the proposed biomimetic tactile sensor is poised to inspire tactile-based human-machine interaction methods.

In this paper, we propose a novel generalized discrete-time variable gain active disturbance rejection control (DTVGADRC) method for then-th order discrete-time nonlinear systems. The error-driven generalized DTVGADRC can dynamically improve the control performances, including generalized discrete-time variable gain tracking differentiator (DTVGTD), generalized discrete-time variable gain extended state observer (DTVGESO), and generalized discrete-time variable gain controller (DTVGC). Furthermore, the stability analysis of generalized DTVGADRC is performed, and the parameters in the variable gain functions are determined by the theoretical analysis. Finally, the generalized DTVGADRC method is applied to parallel teleoperated manipulators, and the experiment results are presented to illustrate effectiveness of the proposed method.

Human-machine interaction technologies are crucial for enhancing human capabilities in the digital world. The hand, as a primary interaction tool, conveys information through gestures and tactile signals. With advancements in flexible electronics and fuzzy systems, it is now possible to achieve interpretable and accurate interactions using hand gestures and tactile information. This paper introduces a fuzzy-encoded, dual-modal soft glove applied to gesture and grasping object classification. The glove, featuring 10 soft pressure sensors and 6 soft bending sensors, is compactly designed and exhibits excellent sensitivity, with average sensitivities of 133 N$^{-1}$for soft pressure sensors and 13deg$^{-1}$for soft bending sensors. The Fuzzy Mamba Encoding (FuME) classification algorithm, inspired by the interpretability and uncertainty resilience of fuzzy systems and the fitting ability of state-space models, was developed. To the best of our knowledge, this work is the first to combine the Mamba structure with fuzzy intelligent systems. Applied to gesture and grasping object classifications, the glove achieved average accuracies of 96.3% and 94.9%, respectively. The fuzzy encoding leverages membership vectors provided by experts to enhance interpretability. These results demonstrate the glove's effectiveness in signal recording and processing and highlight the powerful synergy between flexible electronic technologies and fuzzy systems.

Tactile and proximity sensing is essential for robotic tasks involving human-robot interaction (HRI) and manipulation. However, existing dual-mode sensors often face challenges such as environmental interference, large sizes, and task-specific limitations. This study proposes a dual-mode photoelectric sensor that integrates tactile and proximity sensing. The tactile sensing mechanism is based on a variable optical path structure, while the proximity sensing relies on the surface light reflection. The sensor exhibits a high sensitivity (up to 1.12 V/N), compactness (4 mm thickness), and desirable stability with a drift of less than 1% over 8,000 repetitive cycles under pressures ranging from 0 to 63 kPa. A general tactile-proximity servoing framework is also proposed for the dual-mode sensor array which enables tactile servoing, proximity servoing and hybrid tactile-proximity servoing. Under this framework, parameters can be flexibly adjusted to adapt to different servoing tasks including position and orientation control of the robotic arm's end-effector. In more complex robotic tasks, a real-time fruit ripeness classification method is developed based on the proposed sensor. Using the proposed TPNet, the classification method can achieve an accuracy of 94.4% in a four-level tomato ripeness classification task during grasping.

Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided demonstrations. The model presented in this paper employs a simplistic neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment.

Flexible sensors for joint angle measurement play a crucial role in various human-robot interaction applications. In previous studies, sensors with various sensing mechanisms have been developed. Among them, optical waveguide sensors exhibit high resistance to environmental factors (such as temperature and humidity) and low sensitivity to electromagnetic interference. Researchers have enhanced the sensitivity of optical waveguide sensors to tensile strain by doping other substances (such as graphite) into the optical core material of the optical waveguide. However, the sensitivity of measuring joint angles based on tensile strain principles remains relatively low. In nature, arthropods utilize crack-like structures near their leg joints to perceive minute mechanical stress changes. Here, we propose a curvature sensor based on a Flexible Optical Crack Waveguide Structure (FOCWS) inspired by the arthropod sensory systems. By cutting the optical core, we increase its light power loss during bending strain, thereby enhancing the sensor’s sensitivity to angle measurement. The characteristics of light propagation and geometric parameters were studied through simulation, and experiments were designed to validate the simulation results. The average sensitivity is 0.068 dB/°, which is nearly 300 times higher compared to uncut optical waveguide.

This paper addresses a learning-based path following control scheme for a biomimetic underwater vehicle (BUV) driven by undulatory fins. A dynamic line-of-sight (DLOS) guidance system is designed, which uses a virtual ball with a dynamic radius to detect the reference path. This DLOS system guides our BUV in the path following control and extracts essential information for the Markov decision process (MDP) of the control task. A deep reinforcement learning (DRL) algorithm, sample-observed soft actor-critic (SOSAC) is proposed. The can train out control policy with greater cumulative reward and higher success rate by using two tricks: sample observation and sample diversification. Based on the DLOS system, the MDP of the control task, and a multilayer perceptron (MLP) trained by the SOSAC, our control scheme is established. Experiments show that our BUV can successfully achieve path following control in an indoor pool environment by using this control scheme.Note to Practitioners—The motivation of this paper is to design a practical end-to-end path following control scheme for the BUV driven by undulatory fins, and verify this scheme in a real-world environment. Unlike common autonomous underwater vehicles (AUVs) using axial propellers, the BUVs apply biomimetic propellers such as the undulatory fin. Multimodel wave patterns can be implemented by the undulatory fin, which generates nonlinear thrust and lateral force simultaneously. This propulsive feature makes the driving force on different directions of the BUV to be strong coupled, and it is complicated to convert the outputs of a common controller into waveform parameters of the undulatory fins to control the BUV. Therefore, in this paper, we proposed an end-to-end learning-based path following controller, which observes environmental information and directly generates waveform parameters to control our BUV. Experiments suggest that our control scheme is practical and valid.

In manufacturing, musculoskeletal robots have gained more attention with the potential advantages of flexibility, robustness, and adaptability over conventional serial-link rigid robots. Focusing on the fundamental lifting tasks, a hybrid controller is proposed to overcome control challenges of such robots for widely applications in industry. The metaverse technology offers an available simulated-reality-based platform to verify the proposed method. The hybrid controller contains two main parts. A muscle-synergy-based radial basis function (RBF) network is proposed as the feedforward controller, which is able to characterize the phasic and the tonic muscle synergies simultaneously. The adaptive dynamic programming (ADP) is applied as the feedback controller to address the optimal control problem. The actor-critic structure is applied in the ADP-based controller, where the critic network is trained to approximate the optimal performance index and the actor network is trained to compute the optimal muscle excitations. Furthermore, the convergence and stability of the ADP algorithm are also analyzed. Finally, experiments have been designed to verify the effectiveness of this hybrid controller on an upper limb musculoskeletal system, and the comparisons with other controllers are also illustrated. The results show that the proposed controller can obtain a satisfactory performance for lifting tasks.

Structured light systems are widely used in underwater dense reconstruction due to their excellent accuracy. However, the current related methods mainly focus on fixed positions. The reconstruction performance in motion is insufficient. Therefore, we propose an underwater movable binocular structured light (MBSL) based high-precision dense reconstruction framework, named WaterMBSL, to realize the robot reconstruction while moving. Specifically, an onboard binocular structured light system based on mirror-galvanometer is developed first. Then, a simplified underwater point cloud acquisition algorithm is presented to quickly obtain 3-D information of the scene. Besides, a new underwater motion compensation algorithm combining inertial measurement unit and uniform velocity model is proposed. Moreover, the generalized-ICP point cloud registration algorithm is introduced to achieve accurate motion estimation. Finally, an underwater movable reconstruction platform is developed by integrating the self-designed structured light system with the underwater robot BlueROV for validating the performance of our proposed Water-MBSL. Experimental results show that satisfactory motion reconstruction performance can be obtained.

With the development of collaborative robots, the demand for efficient and safe physical human-robot interaction (pHRI) is significantly increasing. In this paper, a two-loop pHRI controller is proposed to reduce the disagreement in human-robot cooperation and to enhance the level of robot assistance. In the outer loop, a human motion intention estimator is designed, combining the strength of model-free and model-based approaches. It estimates the human's desired movement position and provides the confidence level for the estimated value. Subsequently, the estimated value is tracked by the inner loop controller, and the confidence level is used to adjust the robot's behavior in order to reduce the disagreement. In the inner loop, a neuro-adaptive controller with a variable reference model is designed to achieve the efficient pHRI. A neural network is applied to compensate for the nonlinearity of the robot dynamics, gradually aligning the input-output characteristic of the robot dynamic model with the one of the reference model. To minimize the human-robot disagreement during the collaboration process and to enhance the robot's assistance level, a reinforcement learning method is proposed to adjust parameters of the reference model. The proposed control scheme is implemented on a Franka Panda robot and validated through the point-to-point movement simulation and a real-world human-robot lifting experiment. Results suggest that compared to other methods, the proposed approach can indeed reduce the human-robot disagreement and improve the robot assistance level.

Tactile sensors based on optical waveguides are highly sensitive to pressure, possess good chemical inertness and electromagnetic resistance, and are unaffected by temperature changes in the surrounding environment. Researchers have developed various waveguide structures with multi-level cores to simultaneously measure tactile forces and positions. However, these designs result in thicker waveguides and reduced sensitivity in the lower levels. This study introduces a two-dimensional reticular core optical waveguide for tactile force and positioning sensing, where vertical waveguides intersect each other. The reticular core reduces waveguide thickness and simplifies fabrication processes. The simulation investigates the characteristics of light propagation and geometric parameters. Experimental results confirm the proposed reticular waveguide's force-sensing capability, with an average sensitivity of 0.36 dB/N. Compared to the split-level structure, the reticular waveguide demonstrates more consistent sensitivities along the two shear directions. Utilizing a deep neural network, the spatial resolution achieves approximately 0.72 mm along the X-axis and 1.14 mm along the Y-axis, outperforming the split-level structure.

This article investigates the distributed dynamic event-triggered control of networked Euler-Lagrange systems with unknown parameters. Using the designed dynamic event-triggered control algorithm, the leaderless consensus problem and the containment problem of networked Euler-Lagrange systems are solved, and the estimations of unknown parameters are updated by an adaptive updating law as well. The stability analysis is given based on an appropriate Lyapunov function and the distributed control problem is theoretically solved by the designed control algorithm. The Zeno behavior of the designed dynamic event-triggered method is excluded in a finite-time interval. Compared to some existing results for the event-triggered control of networked Euler-Lagrange systems, these event-triggered methods can be seen as the special cases of the dynamic event-triggered method proposed in this article. Simulation results based on UR5 robots of V-rep show that the proposed method can provide an increase (4.46 ± 3.36%) of the average lengths of event intervals compared to the one of the existing event-triggered methods, which leads to a lower usage of the communication resource. Meanwhile, the time of achieving the consensus/containment and the steady-state control performance are not affected.

In this article, a novel neuro-optimal tracking control approach is developed toward discrete-time nonlinear systems. By constructing a new augmented plant, the optimal trajectory tracking design is transformed into an optimal regulation problem. For discrete-time nonlinear dynamics, the steady control input corresponding to the reference trajectory is given. Then, the value-iteration-based tracking control algorithm is provided and the convergence of the value function sequence is established. Therein, the approximation error between the iterative value function and the optimal cost is estimated. The uniformly ultimately bounded stability of the closed-loop system is also discussed in detail. Moreover, the iterative heuristic dynamic programming (HDP) algorithm is implemented by involving the critic and action components, where some new updating rules of the action network are provided. Finally, two examples are used to demonstrate the optimality of the present controller as well as the effectiveness of the proposed method.

The past decade has seen the rapid development of the robotic fish in many aspects. However, the velocity measurement problem has not been fully addressed, which limits the autonomy of the robotic fish. To this end, an artificial lateral line (ALL) sensor, inspired by the sensory organs of fish, is developed in this article. By measuring the deformation of the sensitive element, the local flow field around the robotic fish is sensed. According to the characteristics of fishlike motions, a fairing structure is proposed to suppress the turbulence noise and yaw motion noise caused by fishlike oscillation of the tail. This structure ensure that the flow measured by the ALL sensor is closer to laminar flow under viscous effects. Furthermore, to measure the magnitude and direction of the robotic fish velocity, an ALL sensor array is assembled by mounting multiple sensors on the robot's surface to sense the flow field distribution. Next, a kinematic-based fusion method is proposed for the array system, which obtained the real-time velocity vector of the robotic fish by solving overdetermined motion equations. The proposed ALL array system is tested on a freely swimming robotic fish, and our method achieves a mean absolute error of 0.018 m/s, a linearity ($R^{2}$) of 0.951, and a position tracking error of 0.085 m. Additionally, the fairing structure is found to improve the signal-to-noise ratio by 116%.

This article presents the design and implementation of an innovative biomimetic underwater vehicle (BUV) and its locomotion controller. Through mimicking a dactylopteridae, the hybrid propulsion BUV is designed with two symmetrical bio-inspired long-fins and a double-joint fishtail. The mechatronic design of the dactylopteridae-inspired BUV with the pectoral long-fins and a double-joint fishtail is first provided. The two flexible long-fins compose the median and/or paired fin (MPF) propulsion, while the fishtail acts as the body and/or caudal fin (BCF) propulsion. Through the coordination of BCF and MPF propulsion modes, the BUV obtains excellent low-speed locomotion stability and also keeps high maneuverability. Moreover, the locomotion control methods based on central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID) are proposed for this BUV. In the end, the experimental results of the multimode motion and closed-loop motion control demonstrate the feasibility and effectiveness of the mechanism and the locomotion control system.Note to Practitioners—The motivation behind this article is the design of a novel biomimetic underwater vehicle (BUV) that possesses low-speed locomotion stability and fast swimming ability, which is suitable for carrying relevant sensors to complete water quality monitoring, biological observation, underwater equipment inspection, underwater structure detection, and other marine tasks. Currently, BUVs are usually designed as only one propulsion mode by caudal fin or paired fins, which makes it difficult to have the advantages of both modes. In order to further study the problem, we designed a dactylopteridae-inspired BUV with the bilateral pectoral long-fins (providing low-speed locomotion stability) and a double-joint fishtail (providing fast swimming ability). A hybrid-driven motion control framework is presented for the BUV based on a central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID). A series of experiments suggests that the mechanism and the locomotion control system are practical and valid. Hopefully, our mechanism and control framework can provide valuable theoretical and technical support guidance to the practicing marine engineer for the codesign of propulsion mode and control.

In this article, we investigate the self-learning robust control synthesis and tracking design of general uncertain dynamical systems. Based on the adaptive critic learning, the robust stabilization method is developed with the help of conducting problem transformation. In addition, by considering the optimal control solution with a discounted cost function, the established method is extended to address the robust trajectory tracking design problem. The Lyapunov stability analysis is also conducted for proving the robustness of the related control plants. Finally, the simulation verification with the three case studies is provided in terms of robust stabilization and trajectory tracking, respectively.

The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility is transformed to the design of a discounted optimal control policy for the nominal plant. The size of the neighborhood with respect to uniformly ultimately bounded stability is discussed. Then, for deriving the approximate optimal solution of the modified Hamilton-Jacobi-Bellman equation, an improved self-learning algorithm under the framework of adaptive critic designs is established. It facilitates the neuro-optimal control implementation without an additional requirement of the initial admissible condition. The simulation verification toward several dynamics is provided, involving the F16 aircraft plant, in order to illustrate the effectiveness of the discounted guaranteed cost control method.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor-critic reinforcement learning framework, the deep deterministic policy gradient actor-critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor-critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method.

Robot-assisted thumb rehabilitation can improve the hand function of stroke patients because the thumb accounts for 40% of hand function. However, existing thumb rehabilitation robots are limited in terms of portability, comfort, adaptability, and independent joint actuation. This article proposes an untethered adaptive thumb exoskeleton that actively assists the 3-degree-of-freedom movements of the thumb. The exoskeleton is composed of an adaptive thumb mechanism and a spherical mechanism. The kinematics, statics, and performances of the adaptive thumb mechanism and the spherical mechanism are analyzed. Experimental validation is performed to test the workspace, self-alignment, interaction forces, admittance controller, and grasping assistance performance of the exoskeleton. The workspace and self-aligning experiments prove that the proposed exoskeleton can achieve a large workspace of the thumb joints and realize self-alignment. The interaction force experimental results show that the exoskeleton can reduce the tangential interaction forces by 76.8% and improve comfort. Finally, the control and delicate grasping assistance experiments validate the exoskeleton’s ability to realize the delicate grasping of the thumb. These characteristics show that the thumb exoskeleton has the potential to assist delicate thumb rehabilitation.

Biomimetic underwater robots have attracted considerable research attention globally, owing to their quieter actuations, higher propulsion efficiency, and stronger maneuverability when compared with conventional underwater vehicles equipped with axial propellers. This article provides a comprehensive survey of current research in this field. First, we review the development status of biomimetic underwater robots in both body/caudal fin (BCF), median/paired fin (MPF), and their hybrid propulsion modes. Then, we outline the motion control methods employed in biomimetic underwater robots, including open-loop swimming control and typical closed-loop control strategies. In particular, we detail our latest studies on the RobCutt series underwater robots. On this basis, some critical issues and future directions are summarized. We predict that biomimetic underwater robots will have excellent prospects in underwater environment exploration and resource utilization.

For stroke patients, hand function assessment is an important part of the hand rehabilitation process. The hand function assessment, however, requires the patient to complete a series of actions under the guidance of the therapist who then scores the patient’s performance. This type of assessment is both time-consuming and highly subjective. Therefore, in order to achieve a fast, objective and accurate assessment, this paper adopts a non-contact infrared imaging device, Leap Motion, to measure the patient’s motion information and then uses these motion information to infer the hand’s rehabilitation level. This paper improves the traditional way of hand function assessment from the following aspects. Only three coherent movements (finger opposition, lift wrist and stretch fingers) are required to complete the assessment, which makes the assessment time shorter and the assessment process easier. At the same time, an assessment algorithm based on the Ensemble Learning is proposed and integrated into the automatic hand function assessment system. In addition, the virtual reality game has been implemented in the assessment system to ensure a satisfactory interaction with patients, which makes the assessment process more interesting and convenient. Using this system, 50 stroke patients underwent clinical trials with the Brunnstrom and Fugl-Meyer assessment scales. The matching rate between the automatic assessment result and the manual Brunnstrom assessment result is 92%, while the matching rate with the Fugl-Meyer assessment result is 82%. Furthermore, Wilcoxon Signed-Rank test and Kappa test are also used to validate the consistency between the automatic assessment results and the manual assessment results. These experiments illustrate that this automatic assessment system is fast, comfortable and reliable.

This article presents a control scheme for robot manipulators with the consideration of output error constraints, unknown dynamics, and bounded disturbances. A modified virtual input variable in the second stage design of the dynamic surface control scheme is proposed, which can enhance the robustness of the controller. Bounded disturbances due to the situations that the base is not well fixed if the robot manipulator is mounted at a mobile platform are considered and suppressed. Besides, the detailed implementation process of the composite learning laws adopted for enhancing the radial basis function neural network is presented. Lyapunov stability analysis verifies that the proposed control scheme ensures the trajectory tracking errors stay within predefined boundaries and parameter estimate errors converge without a stringent condition termed persistent excitation. Experimental results show the superiority of the proposed controller regarding parameter estimation and tracking capabilities.

This article presents a control scheme for the robot manipulator's trajectory tracking task considering output error constraints and control input saturation. We provide an alternative way to remove the feasibility condition that most BLF-based controllers should meet and design a control scheme on the premise that constraint violation possibly happens due to the control input saturation. A bounded barrier Lyapunov function is proposed and adopted to handle the output error constraints. Besides, to suppress the input saturation effect, an auxiliary system is designed and emerged into the control scheme. Moreover, a simplified RBFNN structure is adopted to approximate the lumped uncertainties. Simulation and experimental results demonstrate the effectiveness of the proposed control scheme.

This article addresses a problem of seabed terrain following control (STFC) for an underwater vehicle-manipulator system (UVMS). The motivation is to perform a visual search of marine products closely to seabed in unknown environment. In terms of this issue, we propose a novel and robust STFC framework for our UVMS to maintain an appropriate height to seabed. A nonlinear model predictive control (NMPC) method is formulated to solve the STFC problem. To relieve online computational burden and system noisy influence, Ohtsuka's continuation/generalized minimal residual (C/GMRES) algorithm incorporated with a tracking differentiator (TD) is investigated. In order to improve the following accuracy, the system state prediction part of the NMPC and a long short-term memory (LSTM) network are elaborated to predict future seabed terrain using a depth gauge and an altimeter, respectively. Finally, the three different physical scenarios for STFC problem are established using ROS to demonstrate the robustness and efficiency of the proposed algorithm.

In this paper, an adaptive neural bounded control scheme is proposed for an ${n}$ -link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme.

Variable impedance control has been considered as one of the most important compliant control approaches for its abilities in improving compliance, safety, and efficiency in robot-environment interaction. However, existing variable impedance controllers have deficits in stability guarantee. This article proposes a stability-guaranteed variable impedance control approach for robots with modeling uncertainties based on approximate dynamic inversion (ADI). Novel constraints on variable impedance profiles are given to guarantee the exponential stability of the desired variable impedance dynamics. An ADI-based impedance control law is designed to achieve the desired variable impedance dynamics through the convergence of a variable impedance error. Based on the extended Tikhonovs theorem, it is proven that the closed-loop control system has semiglobal practical exponential stability. The proposed impedance controller can be implemented in a PID form and is appealing for its simple structure, easy implementation, and control stability guarantee. The effectiveness of the proposed variable impedance controller is illustrated by an illustrative example taken on a five-bar parallel robot.

In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme.

The desired impedance dynamics can be achieved for a robot if and only if an impedance error converges to zero or a small neighborhood of zero. Although the convergence of impedance errors is important, it is seldom obtained in the existing impedance controllers due to robots modeling uncertainties and external disturbances. This brief proposes two composite learning impedance controllers (CLICs) for robots with parameter uncertainties based on whether a factorization assumption is satisfied or not. In the proposed control designs, the convergence of impedance errors, reflected by the convergence of parameter estimation errors and some auxiliary errors, is achieved by using composite learning laws under a relaxed excitation condition. The theoretical results are proven based on the Lyapunov theory. The effectiveness and advantages of the proposed CLICs are validated by simulations on a parallel robot in three cases.

In order to leverage the unique advantages of redundant manipulators, avoiding the singularity during motion planning and control should be considered as a fundamental issue to handle. In this article, a distributed scheme is proposed to improve the manipulability of redundant manipulators in a group. To this end, the manipulability index is incorporated into the cooperative control of multiple manipulators in a distributed network, which is used to guide manipulators to adjust to the optimal spatial position. Moreover, from the perspective of game theory, this article formulates the problem into a Nash equilibrium. Then, a neural network with anti-noise ability is constructed to seek and approximate the optimal strategy profile of the Nash equilibrium problem with time-varying parameters. Theoretical analyses show that the neural network model has the superior global convergence and noise immunity. Finally, simulation results demonstrate that the neural network is effective in real-time cooperative motion generation of multiple redundant manipulators under perturbations in distributed networks.

The application of the fast sliding-mode control technique on solving consensus problems of fractional-order multiagent systems is investigated. The design and analysis are based on a combination of the distributed coordination theory and the knowledge of fractional-order dynamics. First, a sliding-mode manifold (surface) vector is defined, and then the fractional-order multiagent system is transformed into an integer-order (namely, first-order) multiagent system. Second, based on the fast sliding-mode control technique, a protocol is proposed for the obtained first-order multiagent system. Third, a new Lyapunov function is presented. By suitably estimating the derivative of the Lyapunov function, the reachability of the sliding-mode manifold is derived. It is proved that the exponential finite-time consensus can be achieved if the communication network has a directed spanning tree. Finally, the effectiveness of the proposed algorithms is demonstrated by some examples.

This paper proposed an adaptive neural admittance control strategy for collision avoidance in human-robot collaborative tasks. In order to ensure that the robot end-effector can avoid collisions with surroundings, robot should be operated compliantly by human within a constrained task space. An impedance model and a soft saturation function are employed to generate a differentiable reference trajectory. Then, adaptive neural network control with position constraint, based on integral barrier Lyapunov function (IBLF), is designed to achieve precise tracking while guaranteeing constrained satisfaction. Utilizing Lyapunov stability principles, we prove that semi-globally uniformly bounded stability is guaranteed for all states of the closed-loop system. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experimental platform. Collisions with surroundings can be avoided in human-robot collaborative tasks.

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.

Robots are widely used to help post-stoke patients conduct rehabilitation training for the motor function recovery. Because of the existence of repetitiveness in the rehabilitation training, a high-order iterative learning controller (ILC) is proposed for one hand rehabilitation robot in this paper. A series of tracking experiments are conducted to verify the effectiveness and superiority of the proposed controller by comparing to the PID controller, the P-type ILC, and the PD-type ILC. Experimental results show that: (1) the average tracking errors of the P-type ILC and the PD-type ILC are smaller than that of the PID controller, and the steady-state performance of the PD-type ILC is better than that of the P-type ILC; and (2) compared to the PD-type ILC, the average transient performance index of the high-order ILC is decreased by 33.9%. The mean value and variance of the tracking error are decreased by 21.1% and 14.4%, respectively.

In this paper, we design and build a power line inspection robot capable of hybrid operation modes. Specifically, the developed robot is able to land on the overhead ground wire (OGW) and to move as the climbing robot. When to negotiate obstacles, it can vertically take off the wire and fly over the obstacles as the unmanned aerial vehicle (UAV). A customized trumpet-shaped undercarriage is used to guarantee that the robot can land and move safely. With the aid of a swingable 2D Laser Range Finder (LRF), the robot can not only determine whether there are obstacles but also detect the position and orientation of the OGW, making it suitable for automatic inspection of power lines. The outdoor experimental results1demonstrate the effectiveness of the robot in landing and obstacle negotiation. In addition, the average power consumption of the robot is much lower than that of traditional flying robots for power line inspection.

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

For most teleoperation tasks, it is desired that the telerobot manipulator follows timely and precisely the reference motion set at the master side. However, the conventional control approach may not guarantee the desired performance when there are dynamic uncertainties, especially when there is a notable variation of the telerobot's payload. In this paper, a neural learning based compensation mechanism has been exploited to overcome the effect of the unknown payload as well as uncertainties associated with the telerobot model and the environment. Guaranteed transient performance has been theoretically established. The deterministic learning technique has been employed, such that the neural learned knowledge can be efficiently reused. We performed comparative experiments and demonstrate the effectiveness of the proposed design techniques.

This paper studies the containment control of multiagent systems (MASs) with multiple dynamic leaders in both continuous-time domain and discrete-time domain. The leaders' motions are described by the nth-order polynomial trajectories. This setting makes practical sense because given some critical points, the leaders' trajectories are usually planned by the polynomial interpolations. In order to drive all followers into the convex hull spanned by the leaders, a PIn-type containment algorithm is proposed (P and I are short for proportional and integral, respectively; Inimplies that the algorithm includes up to the n-thorder integral terms). It is theoretically proved that the PIn-type containment algorithm is able to solve the containment problem of MASs where the followers are described by any order integral dynamics. Compared to the previous results on the MASs with dynamic leaders, the distinguished features of this paper are that: 1) the containment problem is studied not only in the continuoustime domain but also in the discrete-time domain while most existing results only work in the continuous-time domain; 2) to deal with the leaders with the nth-order polynomial trajectories, existing results require the follower's dynamics to be the (n + 1)th-order integral while the followers considered in this paper can be described by any-order integral dynamics; 3) the “sign” function is not employed in the proposed algorithm, which avoids the chattering phenomenon; and 4) both disturbance and measurement noise are taken into account. Finally, some simulation examples are given to demonstrate the effectiveness of the proposed algorithm.

In this paper, a robot, namely iLeg, is designed for the purpose of rehabilitation of patients with hemiplegia or paraplegia. The iLeg is composed of one reclining seat and two leg orthoses, and each leg orthosis has three degrees of freedom, which correspond to the hip, knee, and ankle. Based on this robotic system, two controllers, i.e., passive training controller and active training controller, are proposed. The former takes advantage of the proportional-integral control method to solve the trajectory tracking problem, and the latter employs the surface electromyography signals to achieve active training. Two simplified impedance controllers, i.e., damping-type velocity controller and spring-type position controller, are designed for active training. A perceptron neural network detects movement intentions. The performance of the controllers was investigated with one able-bodied male. The results showed that the leg orthosis tracked the predefined trajectory based on the passive training controller, with the error rates of 0.45%, 0.44%, and 0.27%, respectively, for the hip, knee, and ankle. The active training controller whose loop rate is 6.67 Hz can move the leg orthosis smoothly, and the average recognition error of the perceptron neural network is less than 5%.

The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

Recurrent neural networks, as neurodynamic systems, are a class of connectionist models that capture the dynamics of sequences via cycles in artificial neurons. Since the invention of Hopfield neural network, recurrent neural networks have attracted considerable attention, which marks the beginning of the modern age of neural network studies. Thanks to their inherent nature of parallel and distributed information processing, many computationally intensive applications can be solved by recurrent neural networks in the real-time environment.

In order to implement model-based recognition of human motion intention, dynamics modeling and identification of a lower limb rehabilitation robot named iLeg is investigated. Due to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects: (1) the coupling factors among joints have not been considered in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large; (2) because of the small and complicated feasible region caused by the motion constraints, the traditional initialization strategy, for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient; and (3) the condition number of the observation matrix, calculated from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and, however, further reduction of the condition number has not been considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling factors among joints are considered in the joint friction model by using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed, by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive optimization method based on the optimization of the dynamic model and the exciting trajectories, is proposed to further reduce the condition number. Finally, the performance of the proposed methods is demonstrated by several experiments.

In this paper, a human robot shared control strategy is developed and tested on a Baxter robot. Using the proposed method, the human operator only needs to consider the motion of the end-effector of the manipulator, while the manipulator will avoid obstacle by itself without sacrificing the end effector motion performance. An improved obstacle avoidance strategy based on the joint space redundancy of the manipulator is designed. A dimension reduction method is presented to solve the over defined problem of avoiding velocity to achieve a more efficient use of the redundancy. By employment of an artificial parallel system of the teleoperate manipulator and the task switching weighting factor, the proposed control method enable the robot restoring back to the commanded pose smoothly when the obstacle is removed. By implementing the dimension reduction method, the trajectory of each joint of the manipulator can be controlled at the same time to achieve the restoring task. Thus, the proposed control method can eliminate the impact of the obstacle on the remaining task. Satisfactory experiment results demonstrate the effectiveness of the proposed methods.

Traditional spiking neural networks (SNNs) uses simulated spiking neuron models for computation units. Action potentials (APs or spikes) are generated when the integrated sensory or synaptic inputs to a neuron reach a threshold value. However, spiking generation is not a deterministic process, making current models limited for their potentials and applications. Here we consider the effects of adding probabilistic parameters to the spiking neuron model, which controls the synapses established during spiking generation and transmitting. The Hebbian learning rule is employed for controlling the probabilistic parameters self-adaptation and connection weights associated with the synapses are established using Thorpe's rule during the network learning procedure. The proposed framework combines the essence of stochastic characteristics of the cortical neurons in vivo, the biologically plausibility of Hodgkin-Huxley type neuron dynamics, as well as the computational efficiency of integrate-and-fire (I&F) type neurons. A simple simulation acquired following aforementioned instructions (based on Izhivich's SNN model) exhibits more explicit behavior and robust performance than the original model and deterministic network organizations.

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

In this paper, the firing rate of the neuron based on temporal coincidence coding is estimated for correlation detection. Two cases are considered: the independent inputs are stochastic spike trains that are modeled by the homogeneous Poisson process or the renew process. The situation that the inputs are correlated is also considered and the conditions for the neuron to detect the correlation among inputs are discussed. The results are demonstrated by the simulations.

A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.

A dual neural-network method for the coordination of kinematically redundant robots is proposed in this paper. The performance criteria for single robots provided by Nedungadi and Kazerounian are generalized to a multicriteria form for the coordinated-manipulation system composed of multiple serial manipulators. By optimizing the local joint torques and generalized forces applied on the object/workpiece using a designed weighting matrix, the proposed method achieves the global stability during the coordinated-manipulation process. Moreover, the proposed algorithm has an explicit physical meaning, i.e., both the global kinetic energy of the coordination system and the two-norm of the generalized forces applied on the object are minimized simultaneously. In addition, the physical limits of both joint torques and the generalized forces applied on the object are considered, which makes the original coordination problem become a complicated optimization problem subject to both equality and inequality constraints. Compared with numerical optimization algorithms used in existing literatures, the dual neural-network method has better computational capability to deal with the complicated optimization problem. Finally, illustrative examples are given to show that the proposed method is effective and efficient for the multirobot coordinated-manipulation system.

A recurrent neural network is proposed to deal with the convex optimization problem. By employing a specific nonlinear unit, the proposed neural network is proved to be convergent to the optimal solution in finite time, which increases the computation efficiency dramatically. Compared with most of existing stability conditions, i.e., asymptotical stability and exponential stability, the obtained finite-time stability result is more attractive, and therefore could be considered as a useful supplement to the current literature. In addition, a switching structure is suggested to further speed up the neural network convergence. Moreover, by using the penalty function method, the proposed neural network can be extended straightforwardly to solving the constrained optimization problem. Finally, the satisfactory performance of the proposed approach is illustrated by two simulation examples.

In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational inequality is no longer necessary. By employing Lagrange multipliers, the proposed method can resolve the constrained quadratic programming problems. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed neural network.

A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: Agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.

An adaptive neural network controller is proposed to deal with the task-space tracking problem of manipulators with kinematic and dynamic uncertainties. The orientation of manipulator is represented by the unit quaternion, which avoids singularities associated with three-parameter representation. By employing the adaptive Jacobian scheme, neural networks, and backstepping technique, the torque controller is obtained which is demonstrated to be stable by the Lyapunov approach. The adaptive updating laws for controller parameters are derived by the projection method, and the tracking error can be reduced as small as desired. The favorable features of the proposed controller lie in that: (1) the uncertainty in manipulator kinematics is taken into account; (2) the unit quaternion is used to represent the end-effector orientation; (3) the “linearity-in-parameters” assumption for the uncertain terms in dynamics of manipulators is no longer necessary; (4) effects of external disturbances are also considered in the controller design. Finally, the satisfactory performance of the proposed approach is illustrated by simulation results on a PUMA 560 robot.

A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is reduced dramatically. In addition, the proposed neural network can also be applied to the constrained optimization problems, and it is proved that the convex condition on the objective function of the optimization problem can be relaxed. Finally, the satisfactory performance of the proposed approach is demonstrated by simulation examples.

An adaptive control approach is proposed to deal with the multi-manipulator system consensus problem based on the multi-agent theory. In the current multi-agent literature, agents are assumed to have determined models. However, the real manipulator's dynamics contains uncertain parameters. According to the “linearity-in-parameters” property, the adaptive updating law for uncertain dynamics parameters is derived by the projection method. Then, a decentralized controller is designed based on the backstepping scheme, which only utilizes the information of connected manipulators. By the proposed controller, all the manipulators' joints move towards the same configuration to achieve certain coordination tasks. In addition, performance of the control system is analyzed by the Lyapunov method, and the consensus error is proved to approach zero. Finally, the effectiveness of the proposed scheme is illustrated by simulations on a multiple two-link manipulators system.

Surface electromyography (sEMG) is a significant interaction signal in the fields of human-computer interaction and rehabilitation assessment, as it can be used for hand gesture recognition. This paper proposes a novel MLHG model to improve the robustness of sEMG-based hand gesture recognition. The model utilizes multiple labels to decode the sEMG signals from two different perspectives. In the first view, the sEMG signals are transformed into motion signals using the proposed FES-MSCNN (Feature Extraction of sEMG with Multiple Sub-CNN modules). Furthermore, a discriminator FEM-SAGE (Feature Extraction of Motion with graph SAmple and aggreGatE model) is employed to judge the authenticity of the generated motion data. The deep features of the motion signals are extracted using the FEM-SAGE model. In the second view, the deep features of the sEMG signals are extracted using the FES-MSCNN model. The extracted features of the sEMG signals and the generated motion signals are then fused for hand gesture recognition. To evaluate the performance of the proposed model, a dataset containing sEMG signals and multiple labels from 12 subjects has been collected. The experimental results indicate that the MLHG model achieves an accuracy of$99.26\%$for within-session hand gesture recognition,$78.47\%$for cross-time, and$53.52\%$for cross-subject. These results represent a significant improvement compared to using only the gesture labels, with accuracy improvements of$1.91\%$,$5.35\%$, and$5.25\%$in the within-session, cross-time and cross-subject cases, respectively.

A recurrent neural network is proposed for solving non-smooth nonlinear programming problems, which can be regarded as a generalization of the smooth nonlinear programming neural network used in (X.B. Gao, 2004). Based on the non-smooth analysis and the theory of differential inclusions, the proposed neural network is demonstrated to be globally convergent to the exact optimal solution of the original optimization problem. Compared with the existing neural networks, the proposed approach takes both equality and inequality constraints into account, and no penalty parameters have to be estimated beforehand. Therefore, it can solve a larger class of non-smooth programming problems. Finally, several illustrative examples are given to show the effectiveness of the proposed neural network.

A recurrent neural network for the optimal control of a group of interconnected dynamic systems is presented in this paper. On the basis of decomposition and coordination strategy for interconnected dynamic systems, the proposed neural network has a two-level hierarchical structure: several local optimization subnetworks at the lower level and one coordination subnetwork at the upper level. A goal-coordination method is used to coordinate the interactions between the subsystems. By nesting the dynamic equations of the subsystems into their corresponding local optimization subnetworks, the number of dimensions of the neural network can be reduced significantly. Furthermore, the subnetworks at both the lower and upper levels can work concurrently. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. The proposed method is extended to the case where the control inputs of the subsystems are bounded. The stability analysis shows that the proposed neural network is asymptotically stable. Finally, an example is presented which demonstrates the satisfactory performance of the neural network.

High-voltage power transmission line inspection robot must plan its behavior to detect the obstacles from the complex background according to their types when it is crawling along the power transmission line in order to negotiate reliably. In most cases, robot fulfills the task by its vision system. However, motion blur due to camera motion caused by wind or other unknown causes can significantly degrade the quality of the image acquired. This is a typical kind of the so called image restoration problem, which is a hard problem since no prior knowledge of the motion is available. For this purpose, a novel approach for image restoration is proposed. The restoration procedure consists of two stages: estimation of blur function parameters and reconstruction of images. Image degradation model is proposed first to identify blur function parameters, then a recurrent neural network is used to restore the blurred image. Experiments on real blurred images on power transmission line prove the feasibility and reliability of this algorithm. Our experiments show that the restoration procedure consumes only small amount of computation time.

Real-time control of multi-robot coordination system has attracted a lot of attention in recent years. Traditional numerical algorithm is ineffective to perform this task. In this paper, a dual neural network approach is applied to resolve the coordination problem of two redundant robots. By this approach, the joint torque and distributed load can be obtained by optimizing a multiple criteria, and the physical limits of the joint torque and distributed load can be also incorporated into the control scheme. The dual neural network has a simple structure which is composed of only one layer of neuron array. The network configuration is updated by the command signals of desired acceleration of the grasped object, and the output of the network is the manipulator's joint torque. A simulation example is presented to demonstrate the effectiveness of the dual neural network method.