Search Results (2,566)

To complete scientific experiments on an underwater tension leg platform, a new cable-driven underwater vehicle is proposed, which is subjected to not only unknown underwater disturbances but also time-varying nonlinear cable tractions. To achieve displacement tracking control despite the high-order nonlinearities and matched and mismatched uncertainties with unknown upper bounds, a radial basis function neural network-based adaptive multiple-surface sliding control strategy (RBFNN-AMSC) is proposed. Utilizing a backstepping design procedure and the Lyapunov approach, the system is decomposed into six subsystems, and the stability is ensured. By employing multiple-surface sliding mode control and exponential reaching law design, the robustness and convergence rate of each subsystem are improved. Moreover, with an adaptive radial basis function neural network, the influences of matched and mismatched uncertainties are compensated, which improves the system anti-jamming capability and avoids the “differential explosion” problem. To verify the effectiveness of the proposed approach, numerical simulations are carried out under different conditions, which show that the proposed control strategy can achieve accurate displacement tracking control regardless of complex nonlinear dynamics and various unknown external disturbances. Full article

This paper proposes a unified invariant-set-based reliable control framework for Takagi–Sugeno (T–S) fuzzy systems subject to actuator saturation and faults. The considered model incorporates both matched actuator faults and mismatched external disturbances, which provides a more realistic control setting. To address these challenges, a unified control framework is developed to systematically account for input constraints and actuator fault effects. A sequence of nested invariant ellipsoidal sets, together with corresponding set-dependent control gains, are constructed to guarantee that state trajectories starting within the designed outer invariant sets progressively converge toward a minimized target set. Based on this structure, relaxed LMI-based conditions are derived to compute both the invariant sets and the associated control laws via convex optimization. Finally, numerical examples demonstrate the effectiveness of the proposed method. Full article

Permanent magnet synchronous motors (PMSMs) operating at low speeds are susceptible to parameter mismatches, periodic harmonics, and various internal and external disturbances, which result in steady-state current errors and low-frequency speed oscillations. To address these issues and improve low-speed PMSM performance, an automatic tuning disturbance rejection incremental deadbeat predictive current control (AT-DR-IDPCC) method is proposed. First, an incremental extended-state observer (IESO) is incorporated into the incremental deadbeat predictive current control (IDPCC) framework to estimate and compensate for lumped disturbances caused by resistance and inductance mismatches, thereby improving parameter robustness. Meanwhile, a quasi-resonant controller (QRC) is connected in parallel with the current loop to selectively suppress sixth-order current harmonics induced by inverter nonlinearities and flux harmonics. Furthermore, a deep deterministic policy gradient (DDPG)-based parameter optimization scheme is introduced to automatically tune the controller parameters, overcoming the limitations of conventional trial-and-error tuning and achieving the coordinated optimization of dynamic response, steady-state accuracy, and disturbance rejection capability. Simulation and experimental results demonstrate that, compared with proportional–integral (PI) control and IDPCC incorporating the IESO (IESO-IDPCC), AT-DR-IDPCC reduces the phase current’s total harmonic distortion (THD) by 56.1% and 23.7% while also decreasing the speed fluctuation amplitude by approximately 50% and 20%, respectively. The proposed method significantly enhances the robustness, harmonic suppression capability, and low-speed control performance of PMSM drives. Full article

Attitude estimation is critical for the stability and reliability of launch vehicle flight missions, especially under complex dynamic conditions with external disturbances and sensor uncertainties. To address the limitations of conventional estimation methods that ignore the coupling between thrust vector control (TVC) and attitude states, this paper proposes a robust attitude estimation framework based on the Right Invariant Extended Kalman Filter (IEKF). Two key innovations are incorporated: first, the control model of the launch vehicle is established as a TVC model, which explicitly characterizes the coupling between TVC inputs (thrust magnitude and gimbal deflections) and launch vehicle dynamics, instead of treating TVC effects as external disturbances. Second, TVC motion constraints are introduced into the classic IEKF filtering process, embedding TVC as a deterministic input into the state propagation model to enhance the structural rationality of the estimator. To verify the effectiveness of the proposed method, simulations of the launch vehicle ascent trajectory are conducted, with three comparative configurations tested under normal and sensor anomaly scenarios. The simulation results demonstrate that the proposed attitude estimation method, integrated with TVC modeling and motion constraints, is significantly superior to traditional methods in both accuracy and robustness, effectively suppressing state estimation drift and maintaining stable performance even under sensor degradation or outages. Full article

Respiratory and systemic deterioration after intraventricular hemorrhage (IVH) can be difficult to interpret when airway, infectious, neurogenic, and treatment-related factors develop in parallel. A 31-year-old obese man (body mass index 37.56 kg/m²) arrived late at night with right caudate hemorrhage, massive IVH, and acute hydrocephalus. Initial consciousness disturbance was mild and oxygenation was preserved, so close observation was selected. Approximately 6–7 h after arrival, worsening headache, restlessness, and progression of hydrocephalus prompted bilateral external ventricular drainage (EVD). The right EVD output remained poor despite lowering the drainage level. From Day 2 to Day 3, positional snoring, desaturation, tachypnea, fever, and inflammatory marker elevation developed; oxygenation improved with lateral positioning and head elevation, suggesting obesity-related upper-airway compromise with probable sleep-disordered breathing (SDB). On Day 4, recurrent hypoxemia required intubation, and computed tomography (CT) showed entrapment of the right lateral ventricle, prompting endoscopic clot evacuation. Chest CT after the second procedure showed bilateral lower-lobe consolidation consistent with hospital-acquired pneumonia (HAP), and cefazolin was changed to meropenem. Serial glucose measurements showed no persistent hypoglycemia, and early coagulation profiles were not markedly abnormal. However, cultures, arterial blood gas analysis, lactate measurement, echocardiography, abdominal CT, and autopsy were not obtained. Despite treatment, hyperpyrexia, persistent tachypnea, and vasopressor-dependent hypotension progressed, and he died on Day 7. The terminal course was most consistent with infection-associated circulatory collapse and ventilatory failure, while central hyperthermia and other unexcluded causes may have contributed. This case illustrates diagnostic uncertainty rather than a single confirmed terminal diagnosis and emphasizes early systemic evaluation when respiratory, thermal, and circulatory findings evolve after IVH. Full article

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

A prescribed time-adaptive event-triggered sliding mode control method is proposed for a second-order aircraft antiskid braking system with unmeasurable longitudinal velocity, subject to unknown actuator faults and external disturbances. Based on the time scale transformation technique, a prescribed-time observer is constructed to estimate the unmeasurable longitudinal velocity. A practical prescribed-time super-twisting observer with a saturated gain is designed to estimate the disturbance. Within the prescribed time convergence framework, an adaptive update law and a nonsingular integral sliding surface are developed to compensate for actuator faults. Building on this, a time-varying dynamic threshold event-triggering mechanism is incorporated into the prescribed time-sliding mode control process, while excluding Zeno behavior and reducing the control update frequency. The aforementioned prescribed-time observers and the event-triggered adaptive sliding mode controller form a strict temporal hierarchical architecture. Based on Lyapunov stability theory, it is proved that the closed-loop system is practically prescribed-time stable and that all closed-loop signals are uniformly ultimately bounded. Comparative simulation results verify the effectiveness of the proposed method. Full article

This paper investigates a formation tracking problem for underactuated surface vessels (USVs) subject to collision avoidance and input saturation constraints. Many existing APF-based formation-control approaches formulate collision avoidance using a single reference point or an inter-center distance, which may provide insufficient geometric information during close-range maneuvers. To improve navigation safety, an elliptical disk-based collision avoidance mechanism is developed by introducing safety points at the bow, stern, port, and starboard sides of each USV, such that multiple characteristic-point distance constraints can be simultaneously enforced. To address unknown nonlinearities caused by model uncertainties and external disturbances, a neural network-based observer is designed to estimate unavailable velocity states and lumped disturbances. Distributed control laws are synthesized by integrating artificial potential functions (APFs), the observer, and a backstepping technique. Additional controllers are introduced to address the input saturation and underactuated issues while preserving the collision avoidance capability. Stability of the closed-loop system is rigorously established via Lyapunov theory. Simulation results demonstrate that the proposed approach achieves safer close-range maneuvering performance compared with conventional single-point methods. Full article

This paper presents a robust attitude-tracking control architecture for rigid spacecraft subject to model mismatches and external disturbances. Quaternions are utilized for attitude representation to prevent the gimbal lock associated with Euler angles. While conventional nonlinear dynamic inversion (NDI) relies on Newtonian mechanics and input–output linearization—which inadvertently generates internal zero dynamics and encounters severe control derivative discontinuities at the $q_0=0$ singularity—this study proposes a novel NDI framework derived strictly from Udwadia’s Lagrangian formulation. This approach realizes an exact input-state linearization directly on the 6-degree-of-freedom active holonomic constraint manifold, completely eliminating internal zero dynamics and mathematical singularities. To ensure robustness against physical uncertainties, the singularity-free NDI is augmented with a nonlinear disturbance observer (DOBC) and an outer-loop linear quadratic (LQ) tracking controller. A rigorous composite Lyapunov stability analysis is conducted for the complete closed-loop architecture. The analysis formally guarantees that both the isolated disturbance estimation error and the fully interconnected dual-loop NDI-DOBC system are Uniformly Ultimately Bounded (UUB), even in the presence of realistic, time-varying disturbances with non-vanishing derivatives ($\dot{\mathit{d}}\ne \mathbf{0}$). Comprehensive numerical simulations, parameterized by a physical spherical air-bearing testbed subject to state-dependent gravitational imbalance torques, validate the architecture’s exceptional tracking precision, smooth transient response, and robust disturbance rejection. Full article

Maglev trains are susceptible to various types of operational challenges, including track irregularities, load variations, and actuator faults. It is evident that these factors can compromise suspension performance and even pose a serious risk to operational safety. This paper proposes a prescribed performance event-triggered fault-tolerant control method for the electromagnetic suspension system of a maglev train subject to multiple constraints. A projection-based adaptive extended state observer is designed to estimate the unknown gain caused by actuator faults and load variations, as well as the external disturbance. In light of the disparity in upper and lower safety margins inherent to the suspension gap error, arising from track irregularities, an asymmetric prescribed performance function and an error transformation are devised to ensure that the gap tracking error perpetually complies with the asymmetric prescribed performance constraint. In addressing the issue of rapid variations in the suspension gap, the vertical velocity is also constrained through the implementation of prescribed performance, resulting in a joint constraint framework that encompasses both the gap tracking error and the vertical motion. A dynamic event-triggered mechanism has been incorporated into the backstepping design with a view to reducing unnecessary control updates under limited communication resources, while Zeno behavior has been excluded from the closed-loop system. Within this framework, a dynamic gain adjustment mechanism with an explicitly bounded rate of variation is further developed to achieve smoother gain adaptation. The uniform ultimate boundedness of all closed-loop signals is demonstrated through Lyapunov stability analysis under the prescribed multiple constraints. The efficacy of the proposed method is demonstrated through comparative simulation results. Full article

High-precision tunnel breakthroughs depend critically on the north-seeking accuracy of maglev gyroscopes. However, external disturbances during underground construction often introduce abrupt jumps into rotor current signals, significantly reducing the orientation reliability. Existing signal-processing methods either require manually defined segmentation windows or apply identical denoising strategies to both stationary and disturbed signal intervals, resulting in limited adaptability and suboptimal denoising performance. To overcome these limitations, this study proposes an improved rotor current denoising algorithm based on the MAF-ARIMA framework by incorporating the Bayesian ensemble algorithm for abrupt change, seasonality, and trend (BEAST) and an optimized wavelet transform (OWT). First, the BEAST is employed to automatically detect the structural change point of the rotor current signal, enabling the adaptive segmentation of stationary and jump intervals without manual intervention. Subsequently, empirical mode decomposition is performed, and the OWT applies different denoising parameters to the dominant components of the stationary and jump segments according to their distinct fluctuation characteristics. Finally, moving-average smoothing is adopted to preserve the signal continuity at the segmentation boundary, while the autoregressive integrated moving average (ARIMA) model reconstructs the missing trend component of the jump interval to obtain the complete denoised signal. Comparative experiments using 12 field-collected rotor current datasets demonstrated that the proposed method reduced the standard deviation of the denoised signal by 70.96% and the absolute azimuth error by 50.36% compared with the raw signal, outperforming the optimized Hilbert–Huang transform, HSA-KS, and the original MAF-ARIMA algorithm. By introducing adaptive change-point detection and segment-specific denoising into the existing MAF-ARIMA framework, the proposed method significantly improves the adaptability and denoising performance of maglev gyro rotor current processing under complex tunnel construction environments while preserving the signal continuity and reconstruction accuracy. Full article

A Lyapunov-based framework for stability analysis and synthesis of adaptive neural-network (NN) controllers for a class of uncertain second-order nonlinear systems (SNS) with bounded external perturbations and unmodelled dynamics is presented. Online learning is employed for the reconstruction of the plant nonlinearity with the use of a radial-basis-function (RBF) network whose weights are adapted using a direct adaptation law deduced from a single composite Lyapunov function. The proposed controller couples the weight update to a persistent robustifying action, while the closed-loop stability is guaranteed throughout the learning transient, in contrast to schemes that guarantee stability after learning has converged. Using a composite Lyapunov function in the filtered tracking error and the weight-estimation error, we prove that all closed-loop signals are uniformly ultimately bounded (UUB) and that the tracking error converges to an explicitly characterized residual set whose radius is governed by the network reconstruction accuracy, the disturbance bound and the design gains. A $\sigma$-modification ensures parameter boundedness without persistency of excitation, and a robustness theorem shows that bounded parametric perturbations of the plant preserve stability and enlarge the ultimate bound only gradually (a graceful degradation, rather than a loss of the guarantee). The open-loop plant (a forced double-well Duffing oscillator) is characterized by means of equilibrium and Jacobian analyses. A bifurcation diagram and the largest Lyapunov exponent are presented, which show a chaotic regime (with ${\lambda }_1\approx 0.17$). Numerical experiments indicate that the proposed controller is able to suppress the chaotic motion with a small value of the ultimate bound, and maintain a smooth reference motion with a small and constant RMS error of order ${10}^{-3}$, which is approximately 26 times less than the RMS error obtained with a tuned fixed-gain baseline, and the theoretical dependence of the ultimate bound on the disturbance and the design gains is confirmed by sensitivity sweeps. Full article

In this paper, a special air-ground system consisting of multiple tail-sitters and ground vehicles are constructed under switching topologies and uncertain communication links. For the air-ground system, the ground vehicles serve as the take-off and landing platforms for the tail-sitters. A robust time-varying pinning coordinated controller is developed for the heterogeneous system. The effects of parameter/link uncertainties and external disturbances are effectively attenuated via the devised control law. The robust performance of the air-ground cooperative control system with switching topologies is rigorously validated with the Lyapunov stability criterion. The steady-state tracking deviations satisfy uniform ultimate boundedness and converge within an infinitesimal region around the origin. Simulation tests are carried out to verify that the developed control scheme achieves satisfactory coordination under time-varying cases, with tracking deviations noticeably lower in comparison with conventional control schemes. Full article

This paper proposes an adaptive prescribed-time tracking control strategy based on the dynamic surface technique for hydraulic servo systems subject to time-varying parameters, external disturbances, and output constraints. First, a state-constrained transformation function is introduced to convert the strict output constraint condition into an error boundedness problem. Meanwhile, the dynamic surface control (DSC) technique is employed to effectively avoid the “explosion of complexity” inherent in traditional backstepping design. Second, to tackle complex uncertainties, prescribed-time-driven adaptive update and disturbance estimation laws are separately formulated for precise parameter learning and active disturbance feedforward compensation. Furthermore, a smooth nonlinear robust term is specifically integrated to suppress the residual errors induced by parameter adaptation. Based on the transformed system, a novel control framework integrating error constraints, adaptive parameter estimation, and prescribed-time performance is developed. Rigorous Lyapunov stability analysis proves that the proposed controller not only strictly prevents the system output from violating the constraint boundaries throughout the entire operation, but also ensures that the tracking error converges rapidly and smoothly to a small bounded region near the origin within a time that can be independently predetermined by the designer. Finally, the effectiveness and superiority of the proposed control strategy are fully validated through simulations. Full article

The results of the experiments on the transport process of fluid flow through a pipeline under temperature gradient conditions between the internal and external environments, and on continuous gas generation at the contact boundary of the transported media, are presented in this paper. The test results showed that under non-isothermal flow conditions, a slippage effect will impact flow velocity and pressure, as well as the temperature distributions in variable cross-section pipes. Laboratory experiments were conducted in order to study the effects of the gas nucleus at the pipe walls on the hydrodynamic characteristics of the fluid flow. It is shown that the throughput capacity of the pipe is affected by the temperature difference between the oil and the pipe walls. The test results also demonstrated that at certain temperature gradients on the border layer, the pipe’s capacity reaches its maximum value. Quantitatively, the hydroconductivity of Q/ΔP increased from about 1.45 × 10⁻⁵ m³/(s·MPa) under relatively isothermal conditions to a maximum value of approximately 2.04 × 10⁻⁵ m³/(s·MPa) with a temperature difference in the oil–pipe-wall zone of about 3–5 K, which corresponds to an increase of about 41%. With a further increase in the temperature difference, the hydroconductivity decreased to about 1.64 × 10⁻⁵ m³/(s·MPa) at 10 K and then stabilized in the range of (1.60–1.64) × 10⁻⁵ m³/(s·MPa). This non-monotonic behavior is explained by the temperature-induced release of gas and the formation of a gas-saturated wall zone, which initially reduces the effective resistance of the wall and creates an apparent sliding effect. At high temperature differences, gas accumulation, thermal insulation of the wall area and two-phase flow disturbances limit this effect, which leads to the decrease and subsequent stabilization of the pipe capacity. Full article