Search Results (260)

This study evaluated a discrete staircase implementation of sequential frequency stimulation for steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Five adjacent frequencies separated by 0.2 Hz were presented in consecutive 2 s epochs as ascending-frequency flicker patterns (AFFPs) or descending-frequency flicker patterns (DFFPs) within low-frequency (6–10 Hz) and high-frequency (41–45 Hz) ranges. Offline EEG responses from 15 participants were compared with conventional fixed-frequency stimulation using eight established signal-processing methods: fast Fourier transform (FFT), power spectral density (PSD), canonical correlation analysis (CCA), filter bank CCA (FBCCA), dynamic PSD, short-time Fourier transform (STFT), continuous wavelet transform (CWT), and discrete wavelet transform (DWT). Low-frequency stimulation achieved a higher average normalized classification accuracy than high-frequency stimulation (90.8% vs. 80.6%), while CWT yielded the highest descriptive average accuracy among the evaluated methods; however, post hoc comparisons did not establish CWT as significantly superior to the other evaluated methods. The fixed-frequency, AFFP, and DFFP conditions achieved average normalized accuracies of 89.7%, 85.6%, and 89.5%, respectively. Statistical analysis showed a significant main effect of the signal-processing method but no significant main effect of the stimulation pattern. Three consecutive commands achieved an average accuracy of 91.5 ± 9.6%, whereas the accuracy decreased as the command sequence became longer. The results show that AFFP and DFFP staircase sequences can be detected offline, but they do not prove better classification than fixed-frequency stimulation. The small, uniform sample limits how widely these findings can be applied. Their main value may lie in assisting flexible sequential command design, which needs further testing with larger, more diverse groups and within an online closed-loop BCI system. Full article

Many wearable interface solutions use external computing systems—such as a computer—to infer actions from gestures, limiting feedback to a single vibrotactile or visual channel and rarely reporting on execution times or energy consumption. This paper presents the design and implementation of a wearable single-handed control system based on the Arduino Nano RP2040 Connect device, integrating an inertial measurement unit (IMU) using the Mahony orientation filter for precise attitude tracking and a set of piezo-resistive flex sensors for throttle mapping and gesture-based command execution. A TinyML neural network embedded on the Arduino device classifies hand gestures. The system also delivers multi-channel haptic feedback, creating tactile sensation via Transcutaneous Electrical Nerve Stimulation (TENS) electrodes. Operation with a real quadcopter shows concurrent four-axis continuous control, seven discrete commands, wireless transmission, and command-confirmation feedback. The embedded classifier achieves an accuracy of 0.935 and an F1-score of 0.934, with 1129 parameters and a mean inference time of 1.415 ms, matching results for the best reference classifier, while the complete control cycle remains below 8.031 ms, the sensing-to-transmission latency is below 33.1 ms, and the total glove consumption is 443.55 mW. These results establish a compact multimodal wearable platform whose control mappings can be adapted to other interactive applications such as the UAV teleoperation selected for validation in this work. Full article

In legged robot state estimation without foot-contact measurements, nominal gait phase does not directly indicate the instantaneous validity of foot–ground kinematic constraints, while residual errors in state propagation and kinematic pseudo-measurements can further bias the state estimate. A contact-reliability-aware neural residual Kalman filtering framework is developed while retaining the physics-based state-space model and Kalman recursion. A multi-task temporal estimator infers gait-phase-constrained continuous contact reliability together with dynamic and observation residuals from proprioceptive history sequences. The estimated reliability regulates contact-related process and measurement covariances to adjust the confidence assigned to foot–ground kinematic constraints. The dynamic residual compensates base-velocity prediction errors, and the observation residual corrects predictable errors in foot kinematic pseudo-measurements through the measurement innovation. Simulation results show lower base-state and foot-position errors than the Binary Contact Kalman filter across multiple motion commands. Physical walking and turning experiments show improved horizontal base trajectory estimation and lower terminal drift within the prescribed onboard update period. Full article

A cooperative integrated guidance and control (CIGC) scheme is proposed for multiple- skid-to-turn (STT) vehicles subject to terminal angle constraints and actuator faults. Following the principle of “single-vehicle design first, cooperative extension afterward”, the proposed method integrates prescribed-time sliding mode control, a prescribed-time disturbance observer, dynamic surface control, and nonlinear filtering into a unified IGC framework, ensuring prescribed-time stability of the single-vehicle closed-loop system while enhancing robustness against model uncertainties and actuator faults. Furthermore, a graph-theoretic adaptive cooperative guidance law is developed to generate the desired acceleration command, and a prescribed-time velocity controller is designed to track the command. As a result, all vehicles achieve prescribed-time consensus of the remaining flight time and coordinated arrival at the terminal point at the prescribed time. The stability of the closed-loop system is rigorously established through Lyapunov analysis. Numerical simulation results demonstrate that compared to existing methods, the proposed scheme achieves faster convergence and stronger robustness. Full article

Reliable UAV command-and-control (C2) links require spectrum adaptation that responds to detected incumbent activity while keeping the ground station and aircraft synchronized. This study evaluates an incumbent-aware handoff workflow integrating FFT energy sensing, three-state temporal stabilization, pair-specific spectral admissibility filtering, bidirectional link-margin ranking, and a PROPOSE–ACK–EXECUTE transaction with retries and ordered backups. With a clean 32-window history, the corrected $K=768$ detector produced empirical $P_{\mathrm{fa}}=0.0997$ [0.0980, 0.1014] and $P_{\mathrm{d}}=0.9012$ [0.8995, 0.9029] at $-10$ dB. The point detection target was attained, while the 95% lower confidence bound was 0.8995. A representative logical detector-to-protocol composition at $-10$ dB and 10% independently imposed symmetric IID signaling loss gave a binary detection and recovery probability of 0.844 for the clean-history corrected $K=768$ profile, compared with 0.258 for corrected $K=51$; 95% occupied history reduced the $K=768$ value to 0.121. This composition does not exercise the mobility-coupled packet pathway or a common RF realization across sensing and signaling. In matched protocol trials, the full policy achieved $P_H=0.9321$ [0.9270, 0.9369] while removing EXECUTE; retries, backups, or margin-based ranking reduced reliability and changed the signaling–latency trade-off. Three idempotent EXECUTE transmissions reduced unresolved desynchronization to 0.008, and adding rendezvous recovery increased the eventual-recovery probability to 0.9582 at the cost of a 341-ms 95th-percentile recovery time. At 10% IID loss, the deadline-constrained success probability under the 250-ms internal interruption benchmark was 0.894. Bursty loss produced the largest degradation among the evaluated stress conditions. The results quantify how detector history quality, commit recovery, and protocol redundancy jointly affect reliability and latency. All reported quantitative validation is simulation-based or analytical/software verification; the fifteen-band by five-environment sweep is a static configuration-dependent operating envelope rather than field, hardware, or mobility-coupled validation. The reported detector–protocol integration is logical rather than fully RF-coupled, and no incumbent receiver or secondary-to-incumbent interference path is modeled. Full article

As Internet of Medical Things (IoMT) ecosystems expand, life-critical devices become increasingly vulnerable to microarchitectural leakage attacks that enable adversaries to evade conventional digital authentication mechanisms. Exploiting timing and cache leakage allows adversaries to extract credentials and issue cryptographically valid commands with physiologically unsafe parameters, a systemic weakness we define as the verified attacker problem. This work introduces a dual-layer physiological and side-channel defense architecture that maintains patient safety even under credential compromise. At the perception layer, single-lead 1D electrocardiogram (ECG) telemetry is restored using a 1D Denoising Autoencoder (1D-DAE) to counteract hardware perturbation proxies: actuation jitter, cache eviction block erasures, and password verification timing interference. The 1D-DAE maintains high signal reconstruction fidelity ($\mathrm{PSNR}\approx 29.1–36.7\mathrm{dB}$, $\mathrm{SSIM}\approx 0.81–0.89$), stabilizing diagnostic classification accuracy above $90\%$ across all perturbation conditions. At the actuation layer, a Control Barrier Function (CBF) filter enforces physical safe-set invariance, instantly projecting a malicious pacing command injection ($u_{\mathrm{req}}=300\mathrm{BPM}$) down to a safe $140\mathrm{BPM}$ bound using real-time Quadratic Programming (QP). Hardware profiling yields an end-to-end execution latency of $2.09\mathrm{ms}$ and a low $7.2\mathrm{mJ}$ energy footprint per cycle, confirming real-time feasibility for continuous $360\mathrm{Hz}$ telemetry monitoring and safe closed-loop pacing. Full article

This study presents an asymmetric cooperative perception-and-control architecture in which a sensor-rich mobile master vehicle performs external perception, state estimation, motion planning, and path-tracking control for sensor-light slave vehicles. The proposed vehicle-model-aided LiDAR tracking (VMALT) method combines ego-motion-compensated LiDAR measurements with transmitted speed and steering commands through a kinematic bicycle model. In physical-vehicle experiments, VMALT reduced the slave-position RMSE from 0.36 m with a LiDAR-only constant-velocity Kalman filter to 0.32 m, corresponding to an 11.1% improvement. The estimated state was subsequently used for closed-loop speed and path-tracking control of the physical slave vehicle. Scalability was evaluated using a one-master–two-slave configuration over three separate runs comprising circular and linear paths and geometrically identified line-of-sight-overlap candidates. Concurrent two-target LiDAR availability ranged from 96.465% to 99.934%, with a maximum interior observation gap of 0.10 s. The VMALT trajectories expressed in the GPS coordinate frame followed the position and direction of both slave vehicles, with heading RMSEs ranging from ${1.835}^{\circ }$ to ${4.609}^{\circ }$. In an LTE-tethering communication test, all 2400 UDP packets were returned, with median and 99th-percentile round-trip times of 4.654 and 8.220 ms, respectively. These results demonstrate the feasibility of centralized perception and control for multiple sensor-light vehicles under the evaluated low-speed operating conditions. Full article

Voice interfaces to anthropomorphic robotic arms are typically evaluated without a formal safety layer, or with safety as a post hoc filter trusting the transcript, leaving transcript corruption unaddressed. This paper presents the DAS3 Neuro-Voice Controller, a voice-driven pipeline for a musculoskeletal arm model, evaluated here as a kinematic surrogate for upper-limb exoskeleton and prosthetic control, built on three commitments: on-device intent classification via a 44 M-parameter DistilBERT classifier over a bounded nine-command vocabulary; a deterministic three-layer Gatekeeper enforcing semantic validation, kinematic feasibility, and hard safety constraints independent of classifier confidence; and an upstream Phonetic Interceptor sanitising characteristic ASR mutilations of anatomical and safety terms (“four arm” → “forearm”). Trained on a class-balanced corpus of 3591 expressions and evaluated on fourteen non-native English speakers, the classifier reached 99.95% top-1 accuracy across 1847 oracle-resolvable utterances; the Gatekeeper reduced Priority Override Failure to 0.0%, rejected 100% of out-of-domain speech, and held false-rejection at 2.02%. Against a keyword-spotter baseline the pipeline delivered a 17.4-fold improvement in end-to-end success; the acoustic front-end remains the main weakness. Upstream input repair and downstream deterministic gating emerge as first-class components of safety-critical voice interfaces for assistive systems. Full article

To address issues such as nonlinear strong coupling, time-varying parameters, and external wind disturbances in fixed-wing unmanned aerial vehicles (UAVs) operating in complex flight environments, this study develops a composite attitude control method integrating L₁ adaptive control with an extended state observer (ESO). First, pitch and roll attitude dynamic models considering nonlinear aerodynamic characteristics and channel coupling are established. Based on these models, a composite control architecture is developed in which the L₁ adaptive controller establishes the baseline prediction-error-driven command-tracking loop and introduces matched-uncertainty compensation through a low-pass-filtered adaptive channel, whereas the ESO estimates a generalized extended state and, after removal of the known nominal drift term, provides an independently scaled auxiliary feedforward correction based on the resulting lumped-disturbance estimate. Although the uncertainty contents observed by the two mechanisms may partially overlap, their control actions are coordinated through the residual closed-loop disturbance and the prediction-error-driven adaptation process rather than being independently superimposed at full amplitude. Since the L₁ adaptive law remains driven by the residual state-prediction error after the ESO action, the two mechanisms dynamically redistribute, rather than simply duplicate, the compensation effort. The control performance of the proportional–integral–derivative(PID) controller, the conventional L₁ adaptive controller, and the proposed method is comparatively evaluated through simulations under typical operating conditions, including step response, sinusoidal tracking, composite wind disturbances, and measurement noise. The results show improved transient response and disturbance/noise rejection relative to PID and conventional L₁ control under most of the tested conditions, while the high-frequency tracking benefit is channel-dependent. Overall, the proposed method improves transient response and disturbance/noise rejection while maintaining bounded tracking performance under the stated assumptions. The proposed method provides an effective approach for improving the attitude control performance of fixed-wing UAVs operating in complex environments. Full article

Pan–tilt–zoom (PTZ) cameras are central to visual counter-UAV surveillance: they steer the optical axis to keep a small, agile target centered and adequately resolved, which tightly couples perception with camera control in a closed loop. Learned PTZ controllers face three obstacles: temporal models that treat the observation stream as exogenous and therefore ignore the agent’s own influence on it; loss detection and recovery driven by hand-tuned thresholds with no measurable notion of reliability; and a costly reliance on real flight data and physical hardware for training. We present CMW-Track, which combines an action-conditioned hierarchical Mamba policy that modulates the state-transition operator with the executed PTZ command; an ensemble state predictor that is deliberately lightweight—it predicts only the low-dimensional target state required for PTZ control rather than reconstructing future frames—and is calibrated by split conformal prediction; and Active Uncertainty-Gated Exploration for Recovery (AUGER), a tracking–uncertain–recovery machine gated by conformal-interval violations instead of a tuned confidence threshold. The controller is trained only on procedurally generated trajectories under domain randomization, with no physical PTZ platform in the loop. Evaluated zero-shot in an unseen high-fidelity Unreal Engine 5 environment, CMW-Track attains the highest tracking-success (81.7%) and loss-recovery (85.0%) rates among all evaluated controllers and matches the best completion rate, in real time (median: 2.5 ms per control step). Against five baselines spanning classical, filtered and recurrent-reinforcement-learning control, it improves tracking success by 5.7 pp over the strongest of them in simulation (95% CI: [+4.4, +6.9]), and the margin persists when that baseline is widened to matched policy capacity. The split-conformal bound holds at calibration time, but closed-loop coverage falls 15–21 percentage points below nominal—a direct measurement of exchangeability breakdown that the deliberately redundant trigger absorbs. We therefore report the trigger as calibrated and auditable, not as a deployment-time guarantee. Full article

Truck platooning depends on frequent vehicle-to-vehicle communication to achieve platoon formation, which can create potentially substantial communication and computational burdens. To balance formation performance and communication efficiency, this paper proposes a Proximal Policy Optimization-based Event-Triggered Mechanism (PPO-ETM) for truck platoon formation. Under a predecessor-following topology, each follower uses locally available information and independently determines whether its state should be transmitted. PPO optimizes communication decisions by jointly considering formation errors and transmission costs, while a controller generates acceleration and steering commands. An analytically designed event-triggered filter is further incorporated to guarantee a strictly positive Minimum Inter-Event Time, thereby excluding Zeno behavior. Simulation results show that the proposed framework enables initially dispersed trucks to converge to and maintain the desired formation while avoiding redundant information transmissions. Comparative studies demonstrate that PPO-ETM achieves a favorable balance among formation accuracy, communication efficiency, and learning performance, outperforming other Reinforcement Learning methods. These results indicate that the proposed framework provides a decentralized and scalable solution for communication-constrained truck platooning. Full article

Pneumatic artificial muscles, including McKibben-type actuators, offer high power-to-weight ratio, compliance, and inherent safety, but their nonlinear pressure–contraction behavior, hysteresis, saturation, and load-dependent dynamics make accurate position control challenging. This study develops a practical data-driven workflow that derives a branchwise feedforward compensator and an LQI or PID controller from one open-loop characterization experiment. Quasi-static characterization first identifies a conservative control-ready voltage window. A bounded random excitation within this window is replayed with $4\mathrm{s}$ holds to expose terminal and transient behavior. The same experiment supplies branchwise discrete plant models and a feedforward lookup. Two open-loop-derived transient layers, voltage creep compensation and dynamic pressure referencing, are applied to the raw lookup before simulation. Four controller variants are compared on a common simulated closed-loop benchmark built from the identified plant: a feedforward-only baseline, a branchwise proportional–integral–derivative (PID) baseline, a base linear quadratic integral (LQI) controller with displacement and pressure feedback, and a velocity-state LQI extension with a filtered velocity estimate. A multi-metric optimization score balances tracking RMS, settled oscillation, command activity, saturation, and gain magnitude. The score selects the base LQI within the LQI family. The selected gains and transient layers are deployed in a real-time implementation with manually reduced position gains. The controllers are then evaluated on a common reference stream against the physical actuator. Although simulation metrics cannot be transferred directly to the real system, the combined-metric ranking of the controllers remains unchanged. Full article

Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed electric drive provides a promising solution; however, threshing cylinder blockage, high-frequency load transients, and slope operation make it difficult for conventional energy-management strategies to simultaneously ensure dynamic responses, fuel economy, and battery state of charge (SOC) stability. This study therefore proposes a deep deterministic policy gradient (DDPG)-based reinforcement learning energy-management strategy (RL-EMS) for a range-extended, distributed-drive hybrid tracked combine harvester. First, a full-vehicle dynamic model incorporating eight electric-drive units and strong electromechanical coupling is established. Second, power allocation is formulated as a Markov decision process (MDP), with a multi-objective reward function that accounts for fuel consumption, SOC tracking, and boundary violations; the load-rate-of-change is introduced as a feedforward state. Finally, a supervisory physical layer comprising feasible power projection, safety filtering, and rate limiting is inserted between the policy network output and the physical plant so that the executed command satisfies range extender power, battery SOC, current, and power-slew constraints. Under the standard 1000 s cycle, SOC-corrected energy-equivalent comparison shows that the RL-EMS reduces fuel consumption by 1.5% relative to the adaptive equivalent consumption minimization strategy (A-ECMS) and by 26.6% relative to the constant-torque energy-management strategy (CT-EMS). Under an unseen complex random cycle, the RL-EMS reduces fuel consumption by 5.1% relative to A-ECMS. It also suppresses DC-bus voltage sag during a threshing cylinder blockage transient, demonstrating favorable electromechanical transient response. The proposed method provides a modeling and control reference for the intelligent energy management of range-extended, distributed-drive agricultural machinery. Full article

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions. Full article

This work presents a finite-time adaptive fuzzy control approach for a category of multi-input multi-output nonlinear systems in the presence of input saturation and external disturbances. A hyperbolic function is adopted to convert the unconstrained control command into a bounded signal so that the actuator constraints are strictly respected. Unknown nonlinearities are approximated by fuzzy logic systems, whereas external disturbances are attenuated by adaptive mechanisms together with tanh-based robust terms. In addition, dynamic surface control is incorporated into the backstepping framework, where first-order filters are employed to avoid the computational burden associated with the repeated differentiation of virtual control laws. On this basis, a Lyapunov-based design is carried out to derive the finite-time adaptive control protocol. It is shown that every signal in the resulting closed-loop system is bounded, the closed-loop system is semi-globally practically finite-time stable, and the state variables converge to a small neighborhood of the desired states within a finite settling time, and the control inputs never exceed the prescribed bounds. Simulation results obtained using a representative rigid spacecraft as an example confirm the feasibility and disturbance-rejection capability of the developed method. Full article