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Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance.

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach.

Exoskeleton robots have become a promising tool in neurorehabilitation, offering effective physical therapy and continuous recovery monitoring. The success of these therapies relies on precise motion control systems. Although computed torque control based on inverse dynamics provides a robust theoretical foundation, its practical application in rehabilitation is limited by its sensitivity to model accuracy, making it less effective when dealing with unpredictable payloads. To overcome these limitations, this study introduces a novel realistic model reference computed torque controller that accounts for parametric uncertainties while optimizing computational efficiency. A dynamic model of a seven-degrees-of-freedom human lower limb exoskeleton is developed, incorporating a realistic joint friction model to accurately reflect the physical behavior of the robot. To reduce computational demands, the control system is split into two loops: a slower loop that predicts joint torque requirements based on reference trajectories and robot dynamics, and a faster PID loop that corrects trajectory tracking errors. Coriolis and centrifugal forces are excluded from the model due to their minimal impact on system dynamics relative to their computational cost. The experimental results show high accuracy in trajectory tracking, and statistical analyses confirm the controller’s robustness and effectiveness in handling parametric uncertainties. This approach presents a promising advancement for improving the stability and performance of exoskeleton-based neurorehabilitation.

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.

Immersive human–machine interaction relies on comprehensive sensing and feedback systems, which enable transmission of multiple pieces of information. However, the integration of increasing numbers of feedback actuators and sensors causes a severe issue in terms of system complexity. In this work, we propose a pressure-sensing and feedback glove that enables multi-dimensional pressure sensing and feedback with a minimalist design of the functional units. The proposed glove consists of modular strain and pressure sensors based on films of liquid metal microchannels and coin vibrators. Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. For subsequent tactile interactions, the design of two symmetrically distributed pressure sensors and vibrators at the fingertips possesses capabilities for multi-directional pressure sensing and feedback by evaluating the relationship of the signal variations between two sensors and tuning the feedback intensities of two vibrators. Consequently, both dynamic and static multi-dimensional pressure communication can be realized, and the vibrational actuation can be monitored by a liquid-metal-based sensor via a triboelectric sensing mechanism. A demonstration of object interaction indicates that the proposed glove can effectively detect dynamic force in varied directions at the fingertip while offering the reconstruction of a similar perception via the haptic feedback function. This device introduces an approach that adopts a minimalist design to achieve a multi-functional system, and it can benefit commercial applications in a more cost-effective way.

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives to conventional rigid body structures. In this arena, biomimetic soft systems play an important role in modeling and controlling human hand kinematics without the restrictions of rigid mechanical joints while having an entirely deformable body with limitless points of actuation. In this paper, we address the computational limitations of modeling large-scale articulated systems for soft robotic exoskeletons by integrating a parallel algorithm to compute the exoskeleton’s dynamics equations of motion (EoM), achieving a computation with O(log2n) complexity for the highly articulated n degrees of freedom (DoF) running on p processing cores. The proposed parallel algorithm achieves an exponential speedup for n=p=64 DoF while achieving a 0.96 degree of parallelism for n=p=256, which demonstrates the required scalability for controlling highly articulated soft exoskeletons in real time. However, scalability will be bounded by the n=p fraction.

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With a symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis of the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

We developed a basic lightweight powered wheelchair model using a participatory action design (PAD). We conducted two types of research to identify users’ desires and satisfaction with wheelchairs: one focused on identifying the problems and needs of manual or powered wheelchair users through focus group interviews (FGIs), and the other focused on capturing users’ post-experience feedback with the developed prototypes through usability testing (UT). We structured scenario-based usability tests and questionnaires to capture the participants’ experiences and needs, with the results indicating that both manual and powered wheelchair users emphasized the product’s lightness, design, cost, and battery and motor performance. This user-focused development resulted in higher satisfaction and usability. The significance of this study lies in the active involvement of wheelchair users throughout the research process, incorporating the obstacles and terrains they encounter in real community environments into the development. Future research should assess the usability of the final lightweight powered wheelchair, aiming to improve its affordability to wheelchair users through public benefits.

Recently, textured piezoelectric ceramics have become a hot topic in the field of piezoelectric materials. Due to their high cost-effectiveness, textured ceramics are expected to be the material of choice for the next generation of acoustic transducers. In this study, we investigated the coercive field (Ec), piezoelectric constant (d33), and dielectric constant (ε33) of textured PIN-PSN-PT ceramics under different torques, in response to the demand for the development of composite rod transducer technology for transmitting and receiving. Based on the obtained data, a wideband composite rod transducer was designed and fabricated using textured PIN-PSN-PT ceramics with high performance. Compared with conventional PZT piezoelectric ceramic transducers of the same size, the wideband composite rod transducer made with textured ceramics extends the frequency band to a lower frequency of 6.5 kHz, improves the emission performance by 2 dB, and enhances the reception performance by 2 dB. Compared with conventional PZT piezoelectric ceramics in the same frequency band, the acoustic performance is comparable, but there is a volume reduction of 59.23% and a weight reduction of 49.7%, solving the technical bottleneck of developing composite rod transducers that are miniaturized and lightweight. The research results of this study have important reference value for the engineering application of textured ceramic materials in acoustic transducers.

For elevators, appropriate speed control is pivotal for ensuring the safety and comfort of passengers and optimizing energy efficiency, system stability, and service life. Therefore, the design and implementation of effective speed control strategies are crucial for the operation and management of modern elevator systems. In response to this issue, this paper establishes a dynamic model of an elevator through mechanism analysis. Then, a novel fractional-order sliding mode control strategy with the assistance of a fixed-time adaptive sliding mode observer is proposed. The designed observer can effectively monitor and counteract external perturbations, thereby enhancing the stability and precision of the control system. The fractional-order sliding mode controller can realize a fixed-time convergence property, which is rigorously proven in the sense of Lyapunov. Finally, the effectiveness and superiority of the control scheme are validated by simulations compared with benchmark controllers.

As one of the most widely used thermosets due to its excellent performances, epoxy resin (EP) is widely used in various fields and often employed as a component of composite actuator devices, strengthening their mechanical properties. However, the expanding production of EP inevitably leads to the accumulation of waste end-of-life equipment and the corresponding increasingly serious environmental problems. This review summarizes the recycling strategies of EP, divided into two perspectives: recycling from wastes and sources. Chemical recycling is expected to be the future of waste EP treatment, and we discuss the chemical recycling methods of existing waste EP based on different mechanisms, including the selective cleavage of ester bonds, C–N bonds, and C–O bonds. On the other hand, epoxy vitrimer networks based on various dynamic covalent linkages are also outlined, which can respond to multiple external stimuli and provide materials with recyclability from the origin. Therefore, the use of epoxy vitrimer actuators can prevent waste generation throughout the whole lifecycle. We present some issues of concern in both waste-based and source-based recycling strategies and emphasize the significance of scaling-up. Finally, we summarized the current situation and present some future perspectives with the aim of making practical contributions to environmental issues.

The large number of joints in a continuum manipulator complicates its dynamic modeling, making model simplification inevitable for practical motion control. However, due to external disturbances and internal noise, a controller based on the simplified dynamic model often struggles to meet the desired dynamic performance. To address this issue, this paper proposes an anti-interference control algorithm for continuum manipulators, designed to compensate for parameter uncertainties, external disturbances, and measurement noise. At the same time, the parameters of the algorithm are obtained in the form of solvability of linear matrix inequalities (LMIs). The simulation results show that the algorithm proposed in the paper provides better transient performance and is not affected by the entire disturbance. Experimental results further confirm the effectiveness and robustness of the algorithm.

We developed an electro-mechanical actuator for use as a steering device for railway vehicles. The specifications for a 50 kN-class electro-mechanical actuator capable of steering control for a sharp of a railway radius curve of up to 250 m, and with Direct Drive motor (DD motor) specifications, were derived as a power source to drive it. The DD motor and an electric mechanical steering actuator equipped with it were designed and a prototype was manufactured. As a result of testing laboratory tests on motor and actuator prototypes, all performance requirements were satisfied. We conducted a field test on the actuator prototype by installing it on a railway vehicle to verify the steering angle performance. The steering action of the actuator worked well when running in curved sections, and the steering angles measured for each curve were in line with the target steering angles. The mean error between the target steering angle and the measured steering angle was only 3.4%, indicating that the wheel steering action of the developed steering system was working very well. Therefore, the developed electro-mechanical actuator is expected to be fully utilized as a steering device for railway vehicles by meeting all the performance requirements of the steering system.

Magnetic springs, which can be used to replace traditional mechanical springs, have many advantages, such as necessitating no physical contact, generating no friction, no vibration or noise, and having a long lifespan. Nevertheless, their strong nonlinearity limits their widespread application. In this study, we developed a novel permanent magnet spring to address this issue: a Halbach permanent magnetic spring, with a large levitation force and an approximately linear force characteristic curve. First, we introduce the structure and the parameters of the Halbach permanent magnetic spring. Second, we describe the levitation force performance and the stiffness performance of the Halbach permanent magnetic spring using finite element analysis. Third, we analyze the trends through which different parameters influence the levitation force performance and stiffness performance. Finally, we provide recommendations for the future design of and improvement in the Halbach permanent magnetic spring.

Visual servo grasping technology has garnered significant attention in intelligent manufacturing for its potential to enhance both the flexibility and precision of robotic operations. However, traditional approaches frequently encounter challenges such as task failure when visual features move outside the camera’s field of view (FoV) and system instability due to interaction matrix singularities, limiting the technology’s effectiveness in complex environments. This study introduces a novel control strategy that leverages an asymmetric time-varying performance function to address the issue of visual feature escape. By strictly limiting the range of feature error, our approach ensures that visual features consistently remain within the camera’s FoV, thereby enhancing both transient and steady-state system performance. Furthermore, we have developed an adaptive damped least squares controller that dynamically adjusts the damping term to mitigate numerical instability resulting from interaction matrix singularities. The effectiveness of our method has been validated through grasping experiments involving significant rotations around the camera’s optical axis and other complex movements.

Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, we compare the traditional hierarchical reinforcement learning method with the proposed method. The results prove that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths.

Miniature steerable instruments have the potential to reduce the invasiveness of therapeutic interventions and enable new treatment options. Traditional ways of driving such instruments rely on extrinsic systems due to the challenge of miniaturizing and embedding intrinsic actuators that are powerful enough near the instrument tip or within the instrument shaft. This work introduces a method to amplify the output force of fluidic actuators by connecting their outputs in parallel but distributing them serially in currently underutilized space along the device’s long axis. It is shown that this new approach makes it possible to realize a significant force amplification within the same instrument diameter, producing a 380% higher static force and a further driving motion of the steerable bending segment 55.6° than an actuator representing the current state of the art, all while occupying a similar footprint.

There exists an inaccurate measurement problem in permanent magnet synchronous motors (PMSMs) due to low motor speed operation, high temperatures and humid environments, which will degrade the motion performance and stability of PMSMs. In this study, a model reference adaptive system without position measurement is presented in a PMSM to improve the output performance with external disturbance suppression caused by an environmental change. Firstly, a PI adaptive estimation law is designed to identify the motor speed. Then, a optimization method based on the sliding mode variable structure technique is proposed to realize the stability augmentation of the speed loop by using the parametric fuzzy logic design. To reject the current loop noise, an extended Kalman filter (EKF) is adopted to compensate the input signal in the current loop. The effectiveness of this proposed method is verified via a numerical simulation in the case of different speeds and external loads.

Compressed air ranks among the most expensive forms of energy. In recent decades, increased efforts have been made to enhance the overall energy efficiency of pneumatic actuator systems and develop reliable fault detection methods for preventing energy losses. However, most of the methods developed so far require additional sensors, resulting in extra costs, and/or are not applicable during machine operation, which leads to their limited use in the industry. This article introduces a cycle time-based method for detecting faults in pneumatic actuators through the use of proximity switches, enabling cost-effective monitoring in real time without the necessity of further sensors. A systematic analysis is conducted, expanding the current state of knowledge by detailing the influence of all potential leakage points on the movement times of a pneumatic drive and taking into account the different velocity control strategies (meter-out and meter-in) and operating points expressed via the pneumatic frequency ratio. Previously unassessed specifics of internal leakage, including the impact of pressure profiles and differences between differential cylinders and cylinder with equal piston areas, are also presented. The applicability of the proposed method and its detection limits in an industrial environment are examined using pneumatic assembly machines.

Driven by frequency conversion, the windings of a three-phase permanent magnet synchronous motor (PMSM) contain both odd and even harmonic currents. Due to the motor’s pole–slot conductance modulation, the interaction between the magnetic fields generated by these harmonic currents and the permanent magnet field results in harmonic radial vibrations of the motor. This paper analyzes the three-phase currents of the prototype and derives the radial magnetomotive force (MMF) spatiotemporal models for symmetric harmonic currents. By integrating Maxwell’s magnetic force formula and vibration response formula, the radial vibration models for symmetric harmonic currents are developed. The characteristics of vibrations caused by odd and even harmonic currents, as well as positive sequence and negative sequence harmonic currents, are analyzed separately. A cyclic sequence, low-frequency vibration suppression control method incorporating multiple harmonic current injections was designed. Experimental results of this method are compared with those obtained using an ideal sinusoidal current. Except for the second harmonic vibration, all other vibrations are significantly suppressed, with a maximum suppression rate of 92.28%. The total vibration level is reduced by 12.7619 dB, and the average torque is reduced by 0.67% with the total harmonic distortion of the current at 2.89%. The experimental results show that the vibration method in this paper has little influence on the average torque of the motor, the current distortion rate is small, and the vibration suppression effect is good.

In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was employed to relate the rotational angles and the end-effector’s location, facilitating the design of the actuators. The maximum required torques for joints 2 and 3, responsible for lifting for 12 s, were calculated under quasi-static and dynamic loading conditions. The results showed that the maximum required torques were 126.67 Nm and 58.86 Nm for joint 2 and 3, respectively. The maximum torque for joint 2 occurs when the pitch links are fully extended, whereas the maximum torque for joint 3 occurs when the third link is parallel to the ground. The torques, due to the inertia and Coriolis dynamic terms, were also calculated and found to be lower than those required for the gravitational term. Various maneuvering scenarios, along with Ansys Motion simulation, were analyzed for the verification of the results. Based on the calculated maximum torques, the linear actuators of the EHA were designed. The heavy-duty cobot can be built with the developed actuator proposed in this paper. The total weight of the entire frame was measured to be 14.59 kg, resulting in a high Payload/Weight (P/W) ratio of 1.37. In conclusion, the robot was made lighter and can operate more efficiently, particularly for heavy loads up to 20 kg.

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

The proton exchange membrane fuel cell (PEMFC) is the most widely used fuel cell, but it also has some limitations. One of the research pain points is controlling the oxygen content in PEMFCs. A moderate excess of oxygen boosts electrochemical reaction efficiency, while an appropriate oxygen content ensures system stability. In this paper, a fourth-order nonlinear mathematical model of a PEMFC stack air supply system is established to solve the problem of optimal oxygen excess ratio (OER) control under dynamic load conditions. Based on the model, a nonsingular terminal sliding mode controller (NTSMC) based on a sliding mode observer (SMO) is proposed. The NTSM exhibits superior robustness and performance compared to other sliding mode structures. Meanwhile, the SMO accurately predicts system states, facilitating precise control actions. Additionally, the dual sliding mode surfaces enhance system stability against parameter uncertainties and external disturbances. Our results demonstrate that the proposed controller outperforms traditional ones in terms of robustness and performance, which significantly enhances PEMFC system efficiency and stability.

This study explores the use of shape memory alloys, specifically nickel-titanium (NiTi- Ti-rich), in plate joining processes through riveting. Through the shape memory effect (SME), SMAs offer innovative solutions for joining components, mainly in the aeronautical and aerospace fields, indicating their promising applications. This research presents several characterizations, including differential scanning calorimetry, compression dilatometry, X-ray diffraction using synchrotron radiation, and thermomechanical testing, to assess the feasibility and performance of shape memory alloy rivets. In addition, the samples were subjected to recrystallization heat treatment to evaluate their reusability. The results demonstrated that shape memory alloy rivets are effective, achieving a maximum load of 340 N for two joined components. However, their application is optimal for materials with yield strengths lower than the stress-induced SME. Moreover, the process enhances the joined components’ hardening and increases the rivet’s thermal hysteresis. This research confirms the viability of shape memory alloys for riveting processes, offering a new avenue for advanced joining techniques. The findings provide a foundation for their further development and application in various industries requiring precise and reliable joining methods.

The emergence of intelligent control strategies has made optimization techniques essential for the precise control of DC converters. This study aims to enhance the performance of the Four-Switch Buck–Boost (FSBB) converter through control system optimization. Backpropagation neural networks (BPNNs) have been widely used for optimizing proportional–integral–derivative (PID) controllers. To further improve the FSBB control system, particle swarm optimization (PSO) is employed to optimize the BPNN, reducing dynamic response time and enhancing robustness. Despite these advantages, the PSO method still suffers from limitations, such as slow convergence and poor stability. To address these challenges, chaotic optimization algorithms are integrated with BPNN. The chaotic particle swarm optimization (CPSO) algorithm enhances the global search capability, enabling a faster system response and minimizing overvoltage. This hybrid CPSO-BPNN approach refines the optimization process, leading to more precise control of the FSBB converter. The simulation results show that the CPSO-BPNN-PID controller reaches a steady state more quickly and exhibits superior performance compared to traditional PID controllers.

This paper examined how rotary force feedback in knobs can enhance control over musical techniques, focusing on both performance and user experience. To support our study, we developed the Bend-aid system, a web-based sequencer with pre-designed haptic modes for pitch modulation, integrated with TorqueTuner, a rotary haptic device that controls pitch through programmable haptic effects. Then, twenty musically trained participants evaluated three haptic modes (No-force feedback (No-FF), Spring, and Detent) by performing a vibrato mimicry task, rating their experience on a Likert scale, and providing qualitative feedback in post-experiment interviews. The study assessed objective performance metrics (Pitch Error and Pitch Deviation) and subjective user experience ratings (Comfort, Ease of Control, and Helpfulness) of each haptic mode. User experience results showed that participants found force feedback helpful. Performance results showed that the Detent mode significantly improved pitch accuracy and vibrato stability compared to No-FF, while the Spring mode did not show a similar improvement. Post-experiment interviews showed that preferences for Spring and Detent modes varied, and the applicants provided suggestions for future knob designs. These findings suggest that force feedback may enhance both control and the experience of control in rotary knobs, with potential applications for more nuanced control in DMIs.

This paper introduces an efficient and accurate interpolator for NURBS (non-uniform rational B-spline) curves, addressing the challenge of regulating feedrate under machining accuracy and dynamic constraints, particularly at sensitive corners. A recursive matrix representation and polynomial conversion are utilized to enhance the computation of NURBS curve intermediate points and derivatives. An improved adaptive planning method is presented to adjust the feedrate at sensitive corners, ensuring that chord error and dynamic constraints are met. The method integrates linear acceleration and deceleration stages to mitigate abrupt changes in acceleration and jerk. Additionally, a prediction–correction scheme-based interpolator is developed, employing an asynchronous mechanism to improve computational efficiency. The proposed method’s effectiveness and correctness are validated through simulation tests and machining experiments.

The airborne intelligent hydraulic system is an effective way to solve the ineffective power consumption and temperature rise of an aircraft hydraulic system. An intelligent pump that can work in a variety of modes according to the change of flight conditions is an inevitable requirement for the realization of airborne intelligent hydraulic system, and it is also the development trend of aviation pumps in the future. In this paper, key technologies for aviation intelligent pumps are reviewed. This paper briefly describes its development process and summarizes the research on aviation intelligent pumps from the aspects of the system scheme, working mode, structure form, and control strategy. Finally, the conclusions and trends of the research status of intelligent pumps are given, which can provide a reference for subsequent research on further improving the performance of aviation intelligent pumps.

In this paper, we propose a novel tactile display that can present vibration patterns and thermal stimuli simultaneously. The vibration actuator employs a shape memory alloy (SMA) wire to generate micro-vibration with a frequency control of up to 300 Hz. The micro-vibration is conducted to a tactile pin for amplifying the vibration, to be sufficiently recognized by a user. A thermal stimulation unit, on the other hand, consists of four Peltier elements with heatsinks for heat radiation. Four vibration actuators and a thermal unit are arranged in a flat plane with a size of 20 mm × 20 mm, on which a user places the tip of an index finger to feel the presented vibratory stimuli under different temperature conditions. We conducted an experiment by employing nine subjects to evaluate the performance of the proposed tactile display and also to investigate the effects of temperature on recognizing tactile sensation. The results demonstrated that the proposed device was feasible for the quantitative diagnosis of tactile sensation. In addition, we verified that the sensitivity of tactile sensation decreased with colder stimuli.

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Lower limb exoskeletons have been developed to improve functionality and assist with daily activities in various environments. Although these systems utilize sensors for gait phase detection, they lack anticipatory information about environmental changes, which limits their adaptability. This paper presents a vision-based intelligent gait environment detection algorithm for a lightweight ankle exosuit designed to enhance gait stability and safety for stroke patients, particularly during stair negotiation. The proposed system employs YOLOv8 for real-time environment classification, combined with a long short-term memory (LSTM) network for spatio-temporal feature extraction, enabling the precise detection of environmental transitions. An experimental study evaluated the classification algorithm and soft ankle exosuit performance through three conditions using kinematic analysis and muscle activation measurements. The algorithm achieved an overall accuracy of over 95% per class, which significantly enhanced the exosuit’s ability to detect environmental changes, and thereby improved its responsiveness to various conditions. Notably, the exosuit increased the ankle dorsiflexion angles and reduced the muscle activation during the stair ascent, which enhanced the foot clearance. The results of this study indicate that advanced spatio-temporal feature analysis and environment classification improve the exoskeleton’s gait assistance, improving adaptability in complex environments for stroke patients.

The design of the gear quantity and transmission parameters of a vehicle has large effects on its economical and power performance. This paper mainly researches the gear conditions (including the gear quantity and each gear’s transmission parameters) of two-gear and three-gear AMT (Automated Mechanical Transmission). This research uses Cruise software to build a multi-gear simulation model of a BEV (Battery Electric Vehicle) and adopts the LHS (Latin hypercube sampling) method to design an experiment plan and conduct a simulation experiment. This paper proposes a systematic method for influencing factor analyses and the optimization of transmission parameters, combining fuzzy theory, multiple regression, and particle swarm optimization. The research results show that the gear quantity allowing for optimal overall performance is three. The highest score obtained in the results of the simulation experiment for three-gear AMT is 11.15% higher than that of the two-gear AMT. The optimal design plan for the two-gear AMT is a small ig1 with a big k1, in which case the highest score of the regression model increases by 2.67% compared with that before modeling. The optimal design plan for the three-gear AMT is a big k1 with a big k2, in which case the highest score of the regression model increases by 12.78% compared with that before modeling. Then, this research uses PSO (particle swarm optimization) to further optimize the regression models and compares the difference between the highest scores in the results of the simulation experiment. The difference between the highest scores of the three-gear and two-gear AMT further increases to 21.95% after optimization. As shown in the results, the key factor influencing the performance of two-gear and three-gear AMT is gear quantity.

With the development of mechanical systems in the industrial era, there is an increasing emphasis on the safety and reliability of mechanical equipment. The theory of artificial self-recovery technology has emerged, aiming to enable mechanical equipment to autonomously prevent and repair faults by simulating biological self-recovery mechanisms, thereby enhancing the safety and reliability of industrial production, reducing manual intervention, and promoting the intelligent development of manufacturing. The article mainly explores the application of artificial self-recovery theory in mechanical equipment, elaborating on the achievements in self-recovery technologies such as automatic balancing technology, compensation and self-protection technology for large systems, equipment health assistive technology, and active control technology for sealing devices and hydrostatic bearings, as well as self-repair and self-cleaning technologies. Finally, the paper looks forward to the future development of artificial self-recovery technology, believing that with technological advancements it will play an increasingly important role in the industrial field and promote the development of manufacturing towards self-recovery.

The past three decades have witnessed extensive studies on motion-planning and tracking-control for autonomous vehicles (AVs). There is, however, a lack of studies on effective design methods for AVs, which consist of the subsystems of the mechanical vehicle, tracking-control, motion-planning, etc. To tackle this problem, this paper proposes a design approach for AVs. The proposed method features a design framework with two layers: at the upper layer, a particle swarm optimization (PSO) algorithm serves as a solver to a multi-objective optimization problem for desired AV trajectory-tracking performance; at the lower layer, a coupled dynamic analysis is conducted among the three subsystems, i.e., a nonlinear model for the mechanical vehicle, a motion-planning module, and a controller based on nonlinear model predictive control (NLMPC) for direction control. The simulation results demonstrate that the proposed method can effectively determine the desired design variables for the NLMPC controller and the mechanical vehicle to achieve optimal trajectory-tracking performance. The research findings from this work provide guidelines for designing AVs.

This paper presents an improved method for the non-inertial dynamic analysis of the 3-SPS/U parallel platform (3-SPS/U PM), employing the screw theory and Kane’s method, where S, P, and U denote spherical, prismatic, and universal joints, respectively. The proposed method extends the traditional inertial dynamic analysis to non-inertial systems. First, the generalized screw method is introduced, followed by the derivation of a transformation formula that adapts the screw method to various co-ordinate systems. Subsequently, the velocities and accelerations of each rigid body within the platform under non-inertial conditions are examined by combining the extended screw method with the system’s inverse kinematics model. The extended screw method is not only conceptually simple, but also adaptable to other non-inertial systems. Finally, the standard non-inertial dynamic model of the 3-SPS/U PM is derived through the Kane’s method and validated by the co-simulations with RecurDyn (V9R5) and MATLAB/Simulation (2019b).

In the macro–micro composite motion process, the macro stage achieves rapid motion in a large workspace, while the micro stage realizes precise positioning within a small displacement. The large-stroke and high-speed motion of the macro stage is influenced by nonlinear friction, overshooting, and vibrations, making it challenging to achieve rapid stabilization within the travel range of the micro stage, thereby impacting equipment operation efficiency. This paper proposes a composite controller structure designed to solve the fast point-to-point positioning of the macro stage driven by a linear motor and enhance the trajectory tracking performance of the stage. The proposed composite control algorithm (CCA) includes velocity feed-forward, gain-scheduled proportional integral differential (PID) control, and a plug-in repetitive control method. By employing a tracking differentiator, velocity feed-forward, and a gain-scheduled PID controller, the control algorithm can realize rapid stabilization and positioning for the macro stage. Through velocity feed-forward, gain-scheduled PID control, and a plug-in repetitive controller, the control algorithm can reduce the trajectory tracking error of the stage. Simulations and experimental studies of point response and sinusoidal trajectory tracking are carried out on a macro–micro stage to verify the effectiveness of the composite controller for the linear motor. The experimental results demonstrate that the proposed composite controller effectively reduces the macro stage’s settling time and overshoot, and improves the accuracy of sinusoidal trajectory tracking, which lays a foundation for submicron positioning accuracy in high-speed macro–micro motion stages.

The drag-free satellite plays an important role in the space-based gravitational wave observatory. The capture control of test mass after release is a crucial technology that can affect the success of the mission. The test mass must be released to the center of the electrostatic suspension cage accurately. This paper presents a nonlinear dynamic model of drag-free satellites in Lagrange formalism. A capture control scheme for test mass release phase is proposed based on the state-dependent Riccati equation (SDRE) strategy. To deal with the actuator saturation problem, a nonlinear saturation model is introduced to the dynamics of satellite, while the SDRE strategy is applied to the non-affine system. The effectiveness of the proposed methodology is verified by the numerical simulation for the drag-free satellite.

This study investigated the effectiveness of a dielectric barrier discharge (DBD) plasma actuator operating in burst-in-burst (BIB) mode for flow separation control on a NACA 0015 airfoil. Time-resolved particle image velocimetry measurements were conducted at a Reynolds number of 66,000 and 13° angle of attack. Various BIB signal configurations were tested, with actuation periods of 70 ms and 150 ms, non-actuation periods ranging from 5 ms to 50 ms, and burst frequencies of 300 Hz and 600 Hz. Proper orthogonal decomposition was applied to analyze the flow field dynamics. The results showed that BIB actuation maintained flow attachment with reduced power consumption compared with continuous burst actuation. However, the effectiveness was highly sensitive to the BIB parameters, with some configurations failing to achieve consistent reattachment and becoming unstable. This study reveals complex interactions between actuation vortices and separation processes, highlighting both the potential and challenges of intermittent plasma actuation for efficient flow control.

Increasing the force generation and scalability of soft pneumatic actuators poses significant challenges in robotics applications, especially where high load capacities and precise control are required. This paper presents UniBPAM, a novel, scalable soft pneumatic actuator optimized through 3D printing to enhance force generation. By analyzing design parameters, such as the number of sides, height, and origami factor, UniBPAM achieved a 21.48% increase in force generation. Scalable across diameters from 36 to 144 mm, the actuator demonstrated the ability to lift up to 57 kg with 84.9% strain at larger scales. Additionally, the BiBPAM variant enables bidirectional actuation in robotic joints via motion control, which is realized through PID systems, making it suitable for various robotic applications.

This paper analyzes the performance of an interplanetary CubeSat equipped with a hybrid propulsion system (HPS), which combines two different types of thrusters in the same deep space vehicle, in a heliocentric transfer between two assigned (Keplerian) orbits. More precisely, the propulsion system of the CubeSat considered in this work consists of a combination of a (low-performance) photonic solar sail and a more conventional solar electric thruster. In particular, the characteristics of the solar electric thruster are modeled using a recent mathematical approach that describes the performance of the miniaturized engine that will be installed on board the proposed ESA’s M-ARGO CubeSat. The latter will hopefully be the first interplanetary CubeSat to complete a heliocentric transfer towards a near-Earth asteroid using its own propulsion system. In order to simplify the design of the CubeSat attitude control subsystem, we assume that the orientation of the photonic solar sail is kept Sun-facing, i.e., the sail reference plane is perpendicular to the Sun-CubeSat line. That specific condition can be obtained, passively, by using an appropriate design of the shape of the sail reflective surface. The performance of an HPS-based CubeSat is analyzed by optimizing the transfer trajectory in a three-dimensional heliocentric transfer between two closed orbits of given characteristics. In particular, the CubeSat transfer towards the near-Earth asteroid 99942 Apophis is studied in detail.

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters Kp, Ki, and Kd. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators.

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.

Hydrogels have revolutionized the field of soft robotics with their ability to provide dynamic and programmable responses to different stimuli, enabling the fabrication of highly adaptable and flexible robots. This continual development holds significant promise for applications in biomedical devices, active implants, and sensors due to the biocompatibility of hydrogels. Actuation in hydrogel-based soft robotics relies on variations in material properties, structural design, or a combination of both to generate desired movements and behaviors. While such traditional approaches enable hydrogel actuation, they often rely on complex material design, bringing challenges to hydrogel fabrication and hindering practical use. Therefore, this work seeks to present a simplified and versatile approach for fabricating programmable single-component hydrogel-based soft robotics using an encoded building block design concept and 3D printing. A series of structural building blocks have been designed to achieve various actuation characteristics, including the direction, degree, and kinetics of actuation. By assembling these building blocks into various configurations, a broader range of actuation responses can be encoded, allowing for the fabrication of versatile, programmable soft robotics using a single uniform material through vat photopolymerization 3D printing. This approach enables adaptation to a wide range of applications, providing highly customizable encoding designs.

The On-Load Tap Changer (OLTC) is the only movable mechanical component in a converter transformer. To ensure the reliable operation of the OLTC and to promptly detect mechanical faults in OLTCs to prevent them from developing into electrical faults, this paper proposes a fault diagnosis method for OLTCs based on a combination of Particle Swarm Optimization (PSO) algorithm and Least Squares Support Vector Machine (LSSVM) with multi-feature fusion. Firstly, a multi-feature extraction method based on time/frequency domain statistics, synchrosqueezed wavelet transform, singular value decomposition, and multi-scale modal decomposition is proposed. Meanwhile, the random forest algorithm is used to screen features to eliminate the influence of redundant features on the accuracy of fault diagnosis. Secondly, the PSO algorithm is introduced to optimize the hyperparameters of LSSVM to obtain optimal parameters, thereby constructing an optimal LSSVM fault diagnosis model. Finally, different types of feature combinations are utilized for fault diagnosis, and the impact of these feature combinations on the fault diagnosis results is compared. Experimental results indicate that features of different types can complement each other, making the OLTC state information carried by multi-dimensional features more comprehensive, which helps to improve the accuracy of fault diagnosis. Compared with four traditional fault diagnosis methods, the proposed method performs better in fault diagnosis accuracy, achieving the highest accuracy of 98.58%, which can help to detect mechanical faults in the OLTC early and reduce the system’s downtime.

This paper proposes a path planning and tracking control algorithm for multi-autonomous mobile robot (multi-AMR) systems that addresses the challenges posed by AMRs with a kinematic model. The proposed path planning algorithm is divided into two layers. The upper layer of the algorithm detects spatiotemporal conflicts between the paths of any two AMRs using a spatiotemporal conflict detection tree and the Separating Axis Theorem. The lower layer of the algorithm takes into account the kinematic model of the AMRs, ensuring that the generated paths satisfy the nonholonomic constraints. Furthermore, the lower layer introduces weighted adjustments to the heuristic evaluation, significantly improving the efficiency of the planning process. The proposed tracking control algorithm accounts for the kinematic model of AMRs and various constraints, achieving precise path tracking through model predictive control. The simulation results demonstrate that the proposed path planning and tracking control algorithm can efficiently generate conflict-free paths and achieve precise tracking control that satisfies the nonholonomic constraints of multi-AMR systems.

To address the demand for low noise and high stealthiness in ships and other vessels, this paper innovatively proposes an inertial magnetic levitation actuator based on non-uniform-sized Halbach permanent magnet arrays. To improve control accuracy, it is necessary to establish an accurate analytical model of the magnetic field and then obtain an accurate electromagnetic force model. However, the distortion of the magnetic field at the ends produces end effects, resulting in thrust fluctuations that affect the actuator’s control accuracy. Therefore, considering the end effects is necessary to establish an accurate analytical model of the magnetic field. To analyze the end leakage magnetic field of the Halbach array, the concept of a mechanical pseudo-cycle in the actuator is proposed, and the cycle of a Fourier series is redefined. A completed analytical expression of the Halbach array magnetic field distribution is derived by the new Fourier series, in which the end leakage magnetic field is contained. The accuracy of the proposed method is verified by solving the analytical model of the magnetic field, and the analytical results are compared with finite element simulations and experimental tests.

As network technology continues to advance, network congestion has become an inevitable aspect of network communication. Considering the external interference, unmodeled uncertainty and the interaction between nodes, a multi-bottleneck TCP/AWM network model is established in this paper. A new fixed-time congestion controller was designed by combining a neural network and the backstepping technique. The neural network approximation property is used to eliminate the interference of unmodeled uncertainty and UDP flow in the system. The controller designed in this paper can ensure the stability of the TCP/AWM closed-loop system in a fixed time. Finally, the simulation results demonstrate the effectiveness of the proposed TCP/AWM controller.

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.