Search Results (3,458)

Flying-wing aircraft are more susceptible to wind disturbance due to their smaller wing loading, making gust alleviation critical for flight performance and safety. Conventional control surfaces may exhibit insufficient manipulation efficiency on such configurations, motivating the adoption of active flow control, particularly circulation control (CC) due to its favorable control efficiency. This paper presents an Active Disturbance Rejection Control (ADRC) framework for gust load alleviation (GLA) of flying-wing aircraft equipped with CC actuators, which enables real-time estimation and compensation of both gust disturbance and practical uncertainties and is validated through closed-loop wind-tunnel experiments under various sinusoidal gust conditions. An unsteady aerodynamic model with experimental data is established and simulations are performed for further investigation of alleviation performance and response characteristics under a wide range of gust conditions. Results show that both ADRC and PID exhibit degraded performance at higher gust frequencies and larger gust ratios, but ADRC achieves higher alleviation efficiency across the tested conditions. Furthermore, ADRC maintains satisfactory performance with actuator delays up to 0.04 s and outperforms PID under measurement noise and Dryden turbulence. These findings validate the effectiveness and robustness of ADRC for GLA, underscoring its practical potential for active flow control systems. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

Protection levels for autonomous vehicle localization are traditionally derived from estimator covariances under Gaussian assumptions. These approaches fail in complex urban environments where sensor anomalies produce heavy-tailed, non-Gaussian error distributions. This paper presents a fundamentally different paradigm that reformulates integrity monitoring as a closed-loop control problem. The method computes an instantaneous error rate from three sources: inertial sensor noise, kinematic drift between filter-based and dead-reckoned displacement, and LiDAR scan-map registration quality weighted by a sensitivity factor. This rate drives a saturation-controlled setpoint dynamics, then an adaptive PID controller with entropy-based gain scheduling produces the final protection level. Asymmetric update laws enforce rapid expansion but cautious contraction of safety bounds. Experiments on three UrbanNavDataset sequences (medium-urban, low-urban, deep-urban) demonstrate that traditional covariance-based methods exhibit high integrity risk, while the proposed framework achieves $0.0\%$ risk in moderate environments and $2.3\%$ under extreme degradation. The resulting protection levels are smooth and well-behaved, compatible with modern motion planners. This control-theoretic approach offers a viable alternative to statistical integrity paradigms in challenging real-world conditions. Full article

This paper presents a novel control strategy for hybrid rotor–bearing systems integrating hydrodynamic journal bearings with active magnetic bearings (AMBs) to address the persistent challenge of nonlinear vibrations in high-speed rotating machinery. The study introduces the application of a state-dependent parameter proportional-integral-plus (SDP-PIP) controller designed within a non-minimal state-space framework, offering a significant advancement over conventional control approaches. A four-degree-of-freedom model incorporating short-bearing approximation for hydrodynamic forces and nonlinear electromagnetic force characterization is developed to capture the complex system dynamics. The controller performance is evaluated through numerical simulations over a range of rotational speeds from 130 to 500 rad/s, together with sensitivity analyses under parameter variations and comparisons with a conventional PID controller. The results show that the proposed controller effectively suppresses nonlinear vibrations and stabilizes oil-whirl and oil-whip instabilities over the investigated operating conditions. In comparison with the PID controller, the SDP-PIP controller provides improved vibration attenuation and maintains stable journal motion with lower oscillation amplitudes, particularly near unstable operating regimes. These findings demonstrate the potential of the SDP-PIP control strategy for enhancing the dynamic performance and operational stability of hybrid journal bearing systems. Full article

To addressthe challenge of attitude control in quad-ducted fan unmanned aerial vehicles (UAVs) under coupled disturbances comprising thrust lag, ground effect and a composite wind field during near-ground flight and to mitigate the inherent trade-off between disturbance rejection and noise suppression in fixed-bandwidth extended state observers (ESOs), this paper proposes a robust attitude control method based on a Sigmoid law adaptive-bandwidth extended state observer (AB-ESO). An attitude dynamic model covering the above multi-source disturbances is established, with all uncertainties uniformly treated as lumped disturbances. An adaptive-bandwidth mechanism with filtering and rate-limiting modules is designed for smooth continuous bandwidth tuning. A composite control framework integrating disturbance feedforward, lag compensation and attitude feedback is constructed, and the uniform ultimate boundedness of the closed-loop system is proved. Comparative simulations are conducted against six baseline controllers, including a cascade proportional–integral–derivative (PID) controller, fixed-bandwidth ESOs, incremental nonlinear dynamic inversion (INDI), fast terminal sliding mode control (FTSMC) and a time-varying bandwidth ESO, in a near-ground composite wind scenario. Results show that the proposed method achieves improved comprehensive performance: the three-axis average tracking root mean square error (RMSE) is approximately 72% lower than of the PID controller and 15.8% lower than that of the high-bandwidth ESO, and the control output total variation is reduced by about 27.8%. Monte Carlo verification with 100 random turbulence groups further validates the strong statistical robustness of the proposed method. All validations in this work are based on numerical simulations. This study provides a technical reference for high-precision control of ducted fan UAVs in near-ground environments. Full article

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment. Full article

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. Full article

DC motors remain a subject of significant interest due to their widespread use in industrial automation, robotics, and precision motion-control systems. In many industrial applications, such devices are regulated using PID controllers, though sliding mode control is a viable alternative. However, both approaches have inherent shortcomings. PID, while widespread and easy to tune, underperforms in systems subject to disturbances and model uncertainties. On the other hand, SMC offers excellent disturbance rejection, but typically cannot impose bounds on individual system states, which limits its applicability. To remedy these issues, this paper introduces a new sliding mode control strategy, which is then applied to a class of brushed DC motors. The proposed strategy involves applying a reference model-based SMC to drive the states of the DC motor alongside trajectories obtained from a particular polynomial. These trajectories are defined in a novel way, which always ensures a smooth convergence of each state to its target position while satisfying all necessary state and input constraints. Furthermore, since target states are tracked with the use of a robust sliding mode controller, the effect of uncertainties on system performance is effectively rejected. Full article

Population aging and the rising prevalence of motor disorders are driving demand for assistive lower-limb robotic systems capable of decoding user intention rather than merely providing mechanical support. We present CM-FuseNet, an attention-augmented hybrid Brain–Computer–Muscle Interface (BCMI) that simultaneously fuses cortical concentration indices extracted from electroencephalography (EEG) and lower-limb intention patterns derived from electromyography (EMG) to adaptively control a 4-DOF assistive lower-limb exoskeleton. To eliminate the burden of human-subject ethics review and to ensure reproducibility of the proposed methodology, all validation is performed exclusively on (i) permissively licensed open-access biomedical datasets, (ii) high-fidelity OpenSim 4.5 and MuJoCo 3.1 musculoskeletal–exoskeleton co-simulation, and (iii) limited self-experimentation by the corresponding author with non-invasive consumer-grade devices. Three components are introduced: (i) a log-tanh normalized concentration index CI in (0, 1) derived from the $(P_{SMR}+P_{Mid-Beta})/P_{Theta}$ ratio; (ii) a bidirectional Cross-Modal Transformer (CMT) with eight-head self- and cross-attention; and (iii) a Soft Actor-Critic (SAC) reinforcement-learning controller that adaptively tunes four servo PID gains using a concentration-weighted state. Experiments on the PhysioNet EEGMMIDB, Ninapro DB2/DB7, HuMoD and WAY-EEG-GAL datasets (combining N = 162 trial sessions, 47,520 windows, and five-fold cross-validation) yield a gait-phase classification accuracy of 96.84 ± 1.18%, torque-tracking RMSE of 0.072 ± 0.008 N·m, information transfer rate of 38.6 bits/min, end-to-end latency of 9.4 ms, and a 27.4% reduction in simulated metabolic cost over an EMG-only PID baseline (one-way ANOVA: F(4, 75) = 47.83, p < 0.001; Tukey HSD: p < 0.01 against all baselines). Under high cognitive load, CM-FuseNet preserves accuracy with only a 4.63 percentage-point degradation versus 13.22 percentage points for the EMG-only baseline. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

The subject of this study is the control of a two-degree-of-freedom robotic arm. Initially, the theoretical foundation employed in this study is presented. Subsequently, homogeneous transformation matrices are computed utilizing the Denavit–Hartenberg (D-H) method. Subsequently, the problem of forward kinematics and inverse kinematics is resolved. Subsequently, state space matrices are calculated, and finally, the parameters of the PID (Proportional Integral Derivative) controller are determined to ensure that specific specifications (e.g., overshoot, settling time, steady-state error) are met, even in the presence of disturbances. Full article

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. Full article

The problem of load frequency control (LFC) becomes more severe with the extensive integration of photovoltaic (PV) generation owing to the intermittent nature of the source. In this study, a machine learning approach is developed to design the proportional–integral–derivative (PID) controller of a two-area PV–thermal LFC system, extending a prior proportional–integral (PI) benchmark to full PID action. A Random Forest model is trained to predict the relationship between the six PID gains and the closed-loop integral of time-multiplied absolute error (ITAE), yielding an accurate performance model (test R² = 0.933) that is subsequently searched by a metaheuristic optimizer to determine the controller gains; the resulting controller is termed ML-PID. The novelty of the approach lies in employing the learned model not as a controller or a physical-quantity predictor, as in existing ML-based LFC studies, but as a reusable performance model that maps the controller gains directly to the closed-loop index and guides the PID design. To isolate and quantify the contribution of the learned model, the same three optimizers, namely the Cheetah Optimizer (CO), the Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO), are also applied directly to the plant, yielding purely metaheuristic controllers (CO-PID, GWO-PID, and PSO-PID) that are compared against the machine learning-assisted designs under identical algorithms and computational budget, with CO selected on the basis of the Friedman and Wilcoxon tests. The proposed ML-PID-CO controller attains the minimum ITAE under a step-load disturbance, approximately 70% lower than that of the reference SCHO-PI controller and comparable to the directly optimized controllers, with reduced control effort. Under a simultaneous variation in the plant time constants, it is the most robust of all controllers, exhibiting the smallest Δf₁ undershoot and a performance that degrades about 4.2 times less than that of the reference. The results show that a learned performance model provides a good and reusable basis for PID design. It can be searched over repeatedly once built and reduces the per-design simulation burden relative to direct metaheuristic tuning, while the design is largely independent of the optimizer used. Full article

To address the insufficient control accuracy of traditional vehicle stability control methods under nonlinear conditions, this paper proposes a combined stability control strategy for distributed-drive electric vehicles based on the phase plane method. A two-degree-of-freedom vehicle dynamics model incorporating the Magic Formula tire model is established. The $\beta -\dot{\beta }$ phase plane is selected, and a dynamic stability boundary function is constructed through saddle point analysis and road adhesion coefficient fitting. An instability index is defined to quantify the deviation from the stable state. Based on this index, a hierarchical control strategy is designed: within the stable region, model predictive control (MPC) is employed for yaw moment optimization via differential torque distribution among the four in-wheel motors; when the vehicle enters the unstable region, sliding mode control-based active rear-wheel steering (ARS) is activated. The strategy is validated through CarSim-Simulink co-simulation under step steering and slalom maneuvers. Results show that under the high-speed step steering condition, compared with the uncontrolled case, the combined control reduces the peak yaw rate by 5.3%, the overshoot from 32.86% to 27.38%, the settling time from 9.87 s to 8.02 s, and the oscillation amplitude by 36.5%; under the slalom condition, the yaw rate amplitude is reduced by 5.4%. The proposed strategy effectively improves vehicle handling stability. Full article

This study presents a cost-effective approach for detecting internal leakage faults in hydraulic cylinders by leveraging features extracted from existing control signals, specifically the PID valve input. The key innovation lies in eliminating the need for additional sensors or hardware modifications, making the method suitable for low-cost real-time implementation. Several low-complexity, time-domain features were identified and extracted from the control signal, which reflect changes in system behavior due to internal leakage. These features are designed for edge computing platforms, enabling practical deployment in industrial environments. The proposed method is particularly applicable to systems with known loads and consistent duty cycles, such as hydraulic presses, where deviations in control signal behavior can reliably indicate leakage. However, limitations arise when applied to systems with stochastic or highly variable loading, such as mobile machinery, where external disturbances can obscure leakage effects. This approach enables early fault detection and condition monitoring in hydraulic systems without increasing system complexity or cost. It provides a foundation for predictive maintenance strategies in both stationary and mobile hydraulic equipment, contributing to improved reliability and reduced downtime. Full article