Standard modeling methodologies and classic PI control strategies are poorly suited for doubly salient permanent-magnet (DSPM) motors because of their nonlinear magnetic characteristics and commutation-dependent operating behavior. This study establishes a control-oriented nonlinear model for a 12/8-pole DSPM with an internal radial permanent-magnet arrangement. Current- and position-dependent finite-element flux-linkage Jacobians, rotor-position derivatives, and total electromagnetic torque are organized as three-dimensional lookup tables in the stationary αβ frame, thereby retaining the effects of saturation, armature reaction, interphase coupling, and simultaneous nonzero phase currents. Fuzzy-PI control, implemented as a low-complexity nonlinear gain-scheduling approach, provides a practical alternative to computationally intensive advanced algorithms. To optimize the operational performance, a closed-loop control system is introduced, which utilizes an outer fuzzy-PI loop and an inner current hysteresis loop. This fuzzy-PI speed controller is then compared with a conventionally tuned PI controller under the same conditions. The results demonstrate the usefulness of the FEA-derived model for control evaluation and the favorable transient performance of the fuzzy-PI controller.
In helicopter rotor systems, effective suppression of lead–lag vibration requires dampers with reliable load transfer, appropriate stiffness matching, tunable damping, and efficient energy dissipation. However, conventional lead–lag dampers often suffer from limited stiffness–damping adjustability and sealing-related constraints. Here, we developed a magnetorheological fluid elastomeric (MRFE) damper by integrating magnetorheological fluid with a rubber elastomer. An elastomer system with a target shear modulus of 0.72 MPa was obtained through systematic design of the rubber formulation, rubber–metal bonding, and vulcanization process. Dynamic characterization showed that the dissipated energy increased markedly with amplitude but only slightly with frequency, whereas applied current exerted the strongest influence on the MRFE response. As the current increased from 0 to 1.5 A, the dissipated energy, effective stiffness, and equivalent damping coefficient increased by 1901.5%, 491.0%, and 961.1%, respectively. The zero-current effective stiffness of 0.443 kN/mm closely matched the required baseline stiffness of 0.44 kN/mm. For inverse current prediction, the improved Transformer model with hyperparameters optimized using PSO achieved a 72.04% lower RMSE than the differential evolution-assisted one-dimensional long short-term memory (DE-1DLSTM) model. These results suggest the potential of the MRFE for stiffness-matched support, controllable energy dissipation, and data-driven current prediction in rotor lead–lag vibration mitigation.
Digital twin (DT) technology has become a transformative engineering paradigm for metal-cutting machine tool systems and materials-processing equipment, enabling real-time synchronisation, predictive analytics, and intelligent decision-making throughout the entire production cycle. This systematic review summarises the latest advances in the engineering-oriented implementation of digital twins, with a focus on their modelling frameworks, tool condition monitoring, compensation for geometric, kinematic, thermal and dynamic errors, fault diagnosis and adaptive control, and on the application of this technology to lathes, milling machines and grinding machines. The search for and selection of literature were carried out in accordance with the PRISMA 2020 guidelines. In total, 680 records were identified in Google Scholar in May 2026, of which 197 studies met the inclusion criteria and were synthesised narratively within six thematic sections. The analysis demonstrates that modern digital twin architectures integrate multiphysics modelling, multi-sensor data fusion, and machine learning to create high-precision virtual replicas of physical assets. Predictive maintenance and fault diagnosis systems use machine learning and deep learning to detect incipient faults in feed systems, spindles, and other critical components before failure. The review also analyses existing challenges and outlines future research directions for reliable industrial digital twins.
Aiming at the potential simultaneous occurrence of spin-out and rollover during high-speed cornering on road surfaces, this paper takes distributed-drive electric vehicles as the research object and designs a yaw and roll integrated control (YRIC) strategy based on the vehicle’s stability state. Firstly, the β−β˙ phase plane and ϕ−ϕ˙ phase plane are respectively employed to define the stability control regions and serve as the stability criteria for the vehicle. Secondly, according to the vehicle states within the prediction horizon, the corresponding control mode is selected and the control weighting factors are provided to the upper-level controller. The upper-level controller, based on Model Predictive Control (MPC) theory, computes the optimal control outputs. From the derived desired tire longitudinal forces, the desired additional yaw moment is calculated. The lower-level controller allocates this desired additional yaw moment to the optimal driving torque for each wheel. Finally, co-simulation results using MATLAB/Simulink2021b and Carsim2020 demonstrate that the proposed integrated control strategy possesses strong body attitude correction capability under high-speed cornering conditions and can significantly improve vehicle stability.
In this paper, an ultra-local model-based finite-time sliding mode control (ULM-FTSMC) method is developed for tracking quadrotor position and attitude in the presence of uncertainties and external disturbances. Based on an ultra-local model technique, the proposed ULM-FTSMC scheme consists of an adaptive neural network observer (ANNO) and a non-singular fast terminal sliding mode controller (NFTSMC). The ultra-local model is employed to approximate complex quadrotor dynamics, thereby reducing the complexity of controller design. The ANNO is designed to estimate the state variables required for subsequent control design and compensate for the lumped disturbances. Furthermore, an improved reaching law incorporating a variable exponent and multiple power terms is developed for the nonsingular fast terminal sliding surface, based on which an NFTSMC is constructed to achieve accurate trajectory tracking within finite time. The stability of the closed-loop system and the finite-time convergence of the tracking errors are rigorously established using Lyapunov theory. Finally, comparative numerical simulations with several existing controllers are conducted to demonstrate the effectiveness and superiority of the proposed method.
Soft robotic and wearable systems increasingly require actuators that conform to curved surfaces and stretch with the structures they are mounted on, yet conventional shape memory alloy (SMA) form factors—wires, springs, and sheets—remain limited to one-dimensional contraction or out-of-plane bending. This paper presents the Mesh-Structured Shape Memory Alloy (MeSMA) actuator, a thin, planar, and stretchable actuator in which flat SMA wire spans form a periodic mesh clamped by 3D-printed insulating beads; the beads are fabricated by a print–pause–insert process and define the effective beam length (Le) of each span. Isotonic characterization over Le = 3–7 mm shows that the peak contraction stroke (30–81 mm) and the corresponding optimal payload (7.8–3.9 N) are well approximated by linear functions of Le over the tested range, consistent with a constant critical bending moment at the span level, establishing a single-parameter design rule for tailoring the actuator operating point. Isothermal tests show that temperature tunes the passive secant stiffness approximately threefold. Separately fabricated specimens exhibit a stroke coefficient of variation of about 1% over 30 thermal cycles with matching degradation trajectories. A tubular compression sleeve demonstrates conformal donning and spatially selective compression enabled by the planar, stretchable form factor.
To address the problems of poor steering smoothness and handling stability for four-wheel independent steering (4WIS) vehicles, this paper presents an adaptive nonlinear model predictive control (ANMPC) strategy based on variable steering ratio (VSR). First, a two-degree-of-freedom (2-DOF) vehicle and reference model are established to provide a theoretical basis for controller design. Second, the Hermite interpolation polynomial is used to achieve a smooth transition design of the four-stage VSR. By optimizing the steering ratio curve, the steering smoothness and flexibility across the entire speed range are significantly improved. Furthermore, innovatively, the hyperbolic tangent function (tanh) is employed in combination with the μ-V adaptive weight optimization method to construct an adaptive weight adjustment mechanism for the ANMPC controller. To assess the efficacy of the control strategy, co-simulation experiments are performed using CarSim/Simulink. Simulation results demonstrate that versus conventional MPC, the developed algorithm achieves a 37.75% reduction in yaw rate dynamic response RMSE and a 4.61% decrease in lateral velocity RMSE during low-adhesion double lane-change maneuvers, significantly boosting handling stability and steering smoothness in 4WIS vehicles. The VSR-ANMPC control strategy enhances steering performance substantially while demonstrating exceptional handling stability control across diverse speed and adhesion conditions.
A magnetic-levitation direct-drive oil-free scroll compressor (MLDD-OFSC) eliminates the anti-rotation mechanism to achieve oil-free operation. Still, its large-stroke planar motion introduces strong sensor–DOF coupling and air-gap-dependent stiffness variation that degrade fixed-gain PID performance. This paper proposes a control strategy integrating acceleration feedback linearization, gain-scheduled PID, and coordinate decoupling. An inverse electromagnetic force model is derived to compensate for the nonlinear force–air-gap relationship, linearizing the suspension dynamics. A phase-adaptive gain scheduling law is developed, where gains vary with trajectory phase via a cosine-based mapping. A homogeneous transformation matrix decouples raw sensor signals into independent X, Y, and yaw DOFs. Frequency-domain analysis at three air-gap positions confirms closed-loop stability. Simulations show that the proposed acceleration-linearized gain-scheduled PID (AL_GS_PID) outperforms traditional PID and fixed-gain AL_PID in tracking accuracy. Experiments demonstrate progressive improvement across four configurations—decentralized PID, decoupled PID, fixed-gain AL_PID, and AL_GS_PID—with the full scheme reducing peak errors to 0.043 mm in X and 0.04 mm in Y, corresponding to 74.7% and 33.3% reductions over decentralized PID. These results demonstrate that the proposed strategy effectively addresses coupling and stiffness variation in large-stroke maglev systems under no-load and light-load conditions.
Conventional pneumatic bending actuators cannot perform the in-grasp twisting required to detach mature Agaricus bisporus from the cultivation substrate. Manual harvesting was characterized by measuring fingertip contact forces, fruiting-body rotation, and motion timing using flexible-film force sensors and an inertial measurement unit. The resulting grasp–twist–lift characteristics informed the design of a three-finger segmented variable-angle symmetric oblique-chamber (SVA-SOC) pneu-net soft gripper. Each SVA-SOC actuator integrates upper parallel chambers and lower symmetric oblique chambers that are synchronously pressurized to generate inward bending for grasping; after grasp establishment, differential pressure between the lower oblique chamber groups induces torsional deformation while the grasping pressure is maintained. A reduced-order model based on the Yeoh constitutive model and segmented constant-curvature assumption was validated against finite-element simulations and experiments, yielding root mean square error (RMSE) values of 12.82° and 3.36° for bending and twisting angles, respectively. At 85 kPa, a single actuator generated bending and tangential contact forces of 1.41 and 1.31 N, respectively. The three-finger gripper achieved a maximum target rotation of 44.59°, an initial grasping force of 5.38 N, and a pull-off force of 18.59 N. In 50 harvesting trials, grasping, harvesting, and undamaged harvesting rates were 100%, 94% and 86%, respectively, indicating feasibility for low-damage harvesting of mature Agaricus bisporus.
Accurate dynamic parameters are required for model-based control of lower-limb exoskeletons, but limited excitation, transmission friction, and assembly-dependent uncertainty can degrade conventional estimates. This study examines a two-stage method that combines recursive least squares (RLS) with an adaptive grey wolf optimizer (AGWO). Offline RLS tracks the base-parameter trajectory and expands its post-convergence extrema to construct a finite search space; a non-smooth friction severity index then modulates the GWO convergence schedule. The method was evaluated on a pedestal-mounted, single-degree-of-freedom hip mechanism using a 5 s calibration trajectory and a separate 7 s validation trajectory. Deterministic least squares (LS) and bound-constrained least squares (BCLS) were compared with standard PSO, RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO. Each stochastic method used a population of 30, with 80 iterations (2400 fitness evaluations) and 30 independent seeds. On the independent trajectory, BCLS obtained an RMSE of 0.1152 Nm. Median validation RMSEs were 0.1152, 0.1152, 0.1562, and 0.1516 Nm for RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO, respectively. Thus, the adaptive schedule improved median GWO error by 3.0%, but deterministic BCLS was both more accurate and faster for the present linear-in-parameters model. AGWO is therefore not mathematically necessary for the current convex objective; its potential advantage should be tested with genuinely nonlinear friction parameterizations. The conclusions remain limited to a single-axis pedestal experiment and do not establish performance during human-worn gait.
Ultrasound examination for developmental dysplasia of the hip (DDH) in infants is highly dependent on operator experience, leading to inconsistent imaging quality and poor reproducibility between sonographers. This study proposes an autonomous robotic ultrasound system to improve the standardization and automation of hip ultrasound examinations. The system consists of a robotic arm, a six-axis force/torque sensor, an RGB-D camera and an ultrasound probe, integrating multiple functions including contact force control, visual localization, deep-learning-based segmentation and ultrasound image screening. To ensure stability and safety during scanning, an admittance-based hybrid force/position control strategy is adopted to achieve constant contact force control. For Graf standard plane acquisition, a stage-wise search strategy is designed, in which the search space is progressively narrowed through femoral head searching and multi-angle scanning. The optimal Graf standard plane is then automatically selected by combining image segmentation with a scoring mechanism. A customized hip phantom was used for validation. Experimental results show that the Dice coefficient for femoral head segmentation reaches 0.872, while the average Dice coefficient for multi-structure segmentation reaches 0.866. In 30 autonomous scanning trials, the success rate of Graf standard plane acquisition is 90.0%. Meanwhile, the system can maintain the contact force stably within the target range during scanning, validating the effectiveness of the force control strategy. These results indicate that the proposed robotic system, image recognition algorithm and visual servo control strategy exhibit favorable safety and feasibility, providing an innovative solution for automated infant hip ultrasound examination of DDH.
Magnetorheological elastomers (MREs) exhibit magnetic-field-dependent stiffness and can, therefore, be used to tune the natural frequency of a dynamic vibration absorber through the applied coil current. This study evaluates the extension of a previously developed MRE-based semi-active absorber to two automotive noise, vibration, and harshness transmission paths: road-input-related subframe vibration and engine-induced seat vibration. A standard cylindrical actuator was installed below the subframe of a passenger vehicle and tested on a rough road at 10, 20, and 30 km/h. Vehicle speed was used as the operating-condition feedback variable for current selection, while additional fixed-current measurements were conducted to characterize the current-dependent response. Cabin sound pressure was measured simultaneously. A compact actuator with a lightweight resin housing was also developed for the seat application; engine speed was used as the feedback variable, and the current was selected with reference to the second-order engine excitation. The natural frequencies of both actuators increased with current, although the compact actuator had a smaller tuning range. The actuator-installed conditions produced local, current-dependent reductions in subframe and seat vibration spectra. Changes in cabin sound pressure were smaller, frequency-dependent, and not uniform. The results demonstrate the feasibility of the operating-condition-based tuning of MRE dynamic absorbers for local automotive vibration paths, while also identifying limitations associated with passive installation effects, magnetic-circuit efficiency, packaging, and multi-path cabin acoustics.
Predictive control of pulse-width modulation (PWM)-driven electro-hydraulic proportional valves is challenging because PWM commands affect main-spool motion through pilot-spool dynamics and control-chamber pressure build-up. This study develops a compact pilot-opening-scheduled piecewise affine (PWA) predictor that distinguishes command-level PWM hysteresis from the internal pilot-opening modes. A guarded model-selection procedure, termed PhysGuard, compares a warm-start predictor with a bounded-refinement candidate on a short-horizon rollout record that is excluded from fitting and refinement. Under reset-horizon validation, the retained predictor yields displacement root-mean-square errors (RMSEs) of 0.382 mm and 0.596 mm at 80 ms and 120 ms, respectively. The retained predictor is then embedded in a solver-free kinematic dead-zone-crossing-scheduled event-triggered model predictive control (K-DCS-ET-MPC) law. At each complete-search event, the controller evaluates a finite set of pilot-spool references; an inner servo then converts the selected reference into PWM. Over the full 20 s Simulink benchmark, K-DCS-ET-MPC executes 1025 complete finite-set searches, compared with 5001 for time-triggered model predictive control (TT-MPC), giving a 79.5% reduction in search count. Over the common 5–10 s comparison interval, it achieves the lowest mean absolute error (MAE) and RMSE among nine simulated controllers (0.0371 mm and 0.0569 mm). In the same interval, it executes 373 complete searches, compared with 1251 for full-rate kinematic dead-zone-crossing-scheduled model predictive control (K-DCS-MPC), reducing the search count by 70.18%. Linear finite-set model predictive control produces the smallest maximum absolute error. In a nominal bench test with a sinusoidal reference of 5.5 mm amplitude, the MAE and RMSE averaged over three complete cycles are 0.2358 mm and 0.2781 mm, respectively. The robustness results are simulation-based, and complete-search timing is obtained from host-side replay.
Many practical dynamical models are inherently nonlinear and may exhibit complex growth conditions and uncertainties, which pose considerable challenges to controller design. In this paper, the tracking control issue for a category of high-order nonlinear systems with polynomial-type growth conditions is studied. Through the construction of a new Lyapunov function and the development of a dynamic-gain-based homogeneous control method, a robust tracking controller is designed to achieve the required tracking accuracy while ensuring the global boundedness of all closed-loop signals. As an extension, the system with unknown nonlinear functions is further considered, and a new adaptive tracking controller is constructed. Simulation examples demonstrate the efficacy of the presented control methods.
Friction hysteresis loops are critical for characterizing the nonlinear dynamic behavior of jointed structures. However, their complex nonlinear characteristics make efficient prediction challenging. In this study, a public high-frequency experimental dataset covering diverse operating conditions is adopted to investigate data-driven friction hysteresis loop prediction. Temporal datasets are constructed based on historical displacement information, and temporal deep learning models are developed for one-step-ahead friction force prediction. The prediction performance of TCN and GRU was evaluated across nine operating conditions, demonstrating their capability to accurately model friction hysteresis responses. Both models achieved comparable performance, with average R2 values exceeding 0.997 across all test conditions. TCN delivers marginally better overall accuracy and physical consistency, while GRU yields more stable predictions. Furthermore, physics-oriented metrics, including energy dissipation error and stiffness preservation error, are introduced to evaluate the physical consistency of predicted hysteresis responses. The proposed framework provides an effective data-driven approach for friction hysteresis prediction and offers potential support for nonlinear modeling and digital twin applications.
This paper proposes an adaptive fault-tolerant control strategy for nonlinear switched systems subject to state constraints and time-varying faults. Within a backstepping framework, multiple Lyapunov integral barrier functions are constructed to enforce state constraints, while radial basis function neural networks and adaptive laws compensate for actuator and sensor faults. Under average dwell time switching, the closed-loop system is rigorously proven to achieve fast practical finite-time stability and excellent output tracking performance, ensuring all signals remain bounded without violating constraints. The approach is validated via a variable-load nonlinear spring–damper simulation.
Independently rotating wheelsets (IRWs) are increasingly adopted in low-floor urban rail vehicles because they enable compact bogie layouts and improved curving performance. However, their limited natural self-centering capability can lead to lateral offset from the track centerline, increased flange contact, and degraded lateral guidance performance. This paper presents an LQ-servo-based differential-torque control method for active centering of IRWs using a control-oriented state-space model derived from linearized wheel–rail creep-force relations. Rather than proposing a new control algorithm, this study establishes and experimentally validates an integrated modeling–control–validation framework for differential-torque active centering, providing new quantitative evidence of its effectiveness. The left and right wheel torques are employed as actuation inputs to regulate the lateral displacement of the wheelset, while a conventional proportional–integral–derivative (PID) controller is implemented as a baseline for comparison. The two controllers are evaluated through numerical simulations and validated experimentally using a 1/5-scale IRW roller rig. Compared with the PID controller, the LQ-servo controller achieves faster centering, improved yaw damping, and reduced steady-state lateral offset. In particular, under representative parameter variations, the fixed-gain PID controller loses stability, whereas the LQ-servo controller remains stable and maintains accurate centering, indicating lower sensitivity to the considered parameter variations in the simulation study, while the hardware experiments confirmed real-time implementation in the presence of unmodeled physical effects. These findings support the effectiveness of the proposed modeling and control approach for active centering of independently rotating wheelsets.
In this study, the hardware design, manufacturing, and control of a Two-Wheeled Inverted Pendulum Manipulator (TWIPM) are successfully achieved. The system comprises a two-degree-of-freedom independently driven chassis integrated with a three-degree-of-freedom robotic manipulator. To enable the robot to navigate and reach designated target coordinates on inclined terrain, an autonomous trajectory planning scheme is implemented using the A* algorithm, and the system’s mathematical model is comprehensively updated. To ensure balance stability and enhance robustness against external perturbations, a PI-based supplementary control architecture is proposed to support the core PID controllers governing wheel angular position and chassis tilt. The efficacy of the proposed control strategy is initially validated via numerical simulations under various reference trajectories and disturbance inputs on flat surfaces. Subsequently, experimental validations conducted on a physical testbed featuring an inclined ramp demonstrate the robot’s autonomous trajectory tracking and precise trajectory tracking positioning capabilities, confirming its real-world viability. Finally, future research directions are outlined, focusing on end-effector position optimization, the integration of a dynamic payload estimator under varying weights, and the fully onboard execution of navigation algorithms on the central microcontroller.
Active flow control actuators are critical for improving aerodynamic performance in applications such as aircraft lift enhancement, drag reduction, and maneuverability improvement. However, the plasma synthesis jet actuator (PSJA) is limited by the constraints of electric energy deposition, making it difficult to further improve the jet velocity and mass flow rate. Meanwhile, the jet velocity driven by combustion needs to be further increased. This study preliminarily investigated the characteristics of a combustion-driven SparkJet actuator through experiments; a combustion-driven SparkJet actuator integrates the advantages of plasma actuators and combustion-driven actuators. The actuator employs a continuous methane–air mixture supply ignited by spark discharge. High-speed shadowgraph imaging is used to characterize the jet flow field evolution. The effects of the cavity volume, normalized outlet diameter (d* = d/h), and equivalence ratio on jet performance are systematically examined. As the normalized outlet diameter increases from 0.666 to 1, the jet penetration distance and jet width increase with an increasing normalized outlet diameter, owing to the reduced boundary layer blockage effect at the orifice. However, when the normalized outlet diameter equals 1, both the jet penetration distance and jet width first increase and then decrease with increasing cavity volume; hence, the outlet diameter and cavity have an optimal size. At an equivalence ratio of mixture ≤1, increasing the methane flow rate enhances the volumetric chemical heat release rate, thereby monotonically improving the jet width and penetration distance. The combustion-driven actuator demonstrates a significantly higher jet velocity compared to a conventional plasma actuator under identical geometric parameters; the combustion reaction can effectively amplify the jet energy and velocity of the actuator. Moreover, the combustion-driven SparkJet actuator also has the same working frequency when the actuator volume is the same, indicating that the combustion process does not significantly extend the cycle time. The maximum jet velocity first increases and then decreases with increasing cavity volume; the reason that the maximum jet velocity first increases is that a larger volume results in a greater mixture mass and more released heat; it then decreases due to incomplete combustion occurring in large chambers. These results show that the design standards for the combustion-driven actuator are fundamentally different from those for the plasma actuator, and their optimal performance is related to not only electrical energy density but also combustion performance. The design parameters of the combustion-driven actuator can be optimized to maximize the jet front velocity. The experimental data in this article provides a reference for optimizing the performance of combustion-driven actuators.
Soft robots are particularly suitable for in-pipe inspection, where locomotion must be achieved within confined, curved, and geometrically constrained environments without damaging the pipe wall. In this context, structural compliance is not only a safety feature but also a functional design principle, enabling the robot to adapt to the pipe geometry, maintain distributed contact, and generate locomotion through controlled anchoring and extension–contraction cycles. This study presents the design, fabrication, and experimental validation of an earthworm-inspired soft pneumatic robot for in-pipe inspection. The robot uses inflatable anchoring elements for alternating radial anchoring and pneumatic bellows actuators for extension–contraction cycles, enabling locomotion while maintaining stable contact with the pipe wall. A laboratory-scale prototype was fabricated using additive manufacturing and evaluated with a dedicated pneumatic and control system. Experiments were conducted to determine locomotion performance, operating pressures, actuation timing, friction characteristics, bend negotiation, and load capacity. The optimal anchoring pressure was only 0.1 bar, demonstrating that reliable contact with the pipe wall can be achieved at a very low pneumatic pressure. The robot achieved average locomotion speeds of 21.07 mm/s in horizontal and 20.59 mm/s in vertical PVC pipes, successfully traversed a 90° pipe bend, and demonstrated a maximum vertical load capacity of 1.2 kg. These results demonstrate the feasibility of the proposed biomimetic soft robotic concept for in-pipe inspection and provide a basis for future development toward autonomous operation and industrially relevant testing.
