Search Results (604)

Microgrid integration introduces fast, stochastic disturbances that challenge frequency stability. This paper presents a two-degree-of-freedom fractional-order proportional tilt integral derivative plus one controller (2DOF-FOPTID+1) tuned with a Modified Walrus Optimization Algorithm (MWA) to mitigate frequency deviations while preserving tracking performance. The novelty lies in jointly deploying a 2DOF-FOPTID+1 structure for decoupled tracking and regulation, an MWA-based tuning strategy tailored for resilient frequency control, and the explicit use of aggregated electric vehicles as fast distributed storage to damp frequency and tie-line power excursions; hardware-in-the-loop validation using an OPAL-RT platform is included to demonstrate practical feasibility. The controller is evaluated under step and random load variations, and robustness is examined for $\pm 25\%$ parameter perturbations and stochastic renewable inputs. Compared with the strong baselines PID, FOPID, 2DOF-PID, and FOPTID, the proposed approach reduces settling time by up to 39.27% and lowers peak-to-peak frequency deviation by about 20.88% under these operating scenarios, indicating a practical and effective solution for enhancing frequency resilience in microgrid-integrated power systems. Full article

Constant-speed control of hydraulic retarders is essential for improving driving safety and reducing driver workload on long downhill roads. For heavy-duty battery electric trucks (BETs), regenerative braking provides a fast-response braking source and enables energy recovery, offering the potential to improve both speed regulation and energy efficiency. This study proposes a two-mode constant-speed downhill control framework for BETs. In the retarder braking mode, a variable-argument proportional–integral–derivative (VAPID) controller is employed to regulate the hydraulic retarder, with its parameters optimized by an improved seeker optimization algorithm (ISOA). In the cooperative braking mode, a parallel dual-controller structure is adopted, where the retarder is governed by ISOA-VAPID and regenerative braking is regulated by a fuzzy-tuned PD controller according to real-time battery states. To further improve energy recovery, an optimization-based AMT gear-shifting schedule and coordinated strategy are incorporated. The proposed framework is validated through offline simulations, sensitivity analysis, and driver-in-the-loop experiments under constant-slope, variable-slope, and real-world downhill road conditions. Results show that the retarder braking mode outperforms benchmark methods in steady-state accuracy and dynamic response. In the cooperative braking mode, braking energy is effectively recovered while the battery charging load under unfavorable battery states is reduced. Moreover, AMT gear shifting improves energy recovery efficiency with negligible influence on constant-speed performance. Full article

Laser frequency stabilization underpins precision metrology, optical atomic clocks, quantum optics, and laser spectroscopy. In recent years, field-programmable gate arrays (FPGAs) have become attractive for this task because signal generation, phase-sensitive detection, digital filtering, feedback control, lock monitoring, and automatic re-locking can be integrated on compact and reconfigurable platforms. This review examines recent progress in FPGA-based laser frequency stabilization from four linked perspectives: stabilization principles, digital implementation, system architecture, and intelligent control. We first summarize representative error-signal generation methods, including Pound–Drever–Hall locking, saturation absorption spectroscopy, frequency modulation spectroscopy, and modulation transfer spectroscopy. We then discuss the FPGA functions that determine practical performance, such as data acquisition, direct digital synthesis, digital demodulation, proportional-integral-derivative (PID)/infinite impulse response (IIR) filtering, latency management, and lock-state monitoring. Mixed-signal, all-digital, distributed, and machine-learning-assisted systems are compared to show how bandwidth, latency, stability, integration, cost, and automation are balanced in different designs. This review closes by identifying remaining challenges in analog-to-digital converter/digital-to-analog converter (ADC/DAC) resolution, converter noise, loop latency, actuator bandwidth, long-term robustness, and algorithm portability, and by outlining future directions toward low-latency, software-defined, and intelligent stabilization platforms. Full article

This paper presents a unified comparative study of various controllers, including proportional–integral–derivative (PID), Fractional-Order PID (FOPID), Internal Model Control (IMC) controllers, and Tilt–Integral–Derivative (TID) controllers, for the regulation of mean arterial blood pressure (MABP). The controllers are optimally tuned by using a single metaheuristic approach, namely the Pelican Optimization Algorithm (POA), ensuring a fair and consistent comparison. The POA optimizes the objective function using standard error indices (ITAE, IAE, and ISE) along with transient characteristics. The aforementioned controllers are then evaluated under varying patient conditions for different patient categories, including sensitive, nominal, and insensitive, and their performance is systematically compared with one another and with the reported methods from the existing literature. The simulation results demonstrate that IMC offers fast settling with minimal overshoot, FOPID improves robustness through fractional dynamics, and the TID controller provides the smoothest transient response and disturbance rejection across all patient categories. The results confirm the effectiveness of advanced control strategies over conventional PID and highlight the potential of POA-tuned TID control for reliable and patient-specific MABP regulation in critical care applications. Full article

Path tracking control and algorithm portability for agricultural robots serve as the core technological foundation for achieving precision and automation in farming operations, playing a critical role in ensuring food security and enhancing production efficiency. This paper systematically reviews recent technological advancements in the field. It first elucidates the fundamental theories and technical components of path tracking control, providing detailed analyses of the characteristics and limitations of traditional methods such as Proportional-Integral-Derivative (PID) control, model predictive control (MPC), sliding-mode control (SMC), and the Stanley algorithm. Subsequently, it focuses on innovations in intelligent technologies, exploring the integration trends of adaptive control and intelligent learning algorithms, with particular emphasis on the combined applications of reinforcement learning, deep learning, and intelligent control methodologies. The paper clarifies the significance of algorithm portability and summarizes the current applications and performance differences among various algorithms. The study concludes that traditional methods demonstrate stability and reliability in structured scenarios, while advanced intelligent approaches exhibit stronger adaptability in complex environments, albeit facing challenges such as data dependency and real-time deployment requirements. Future technological developments will prioritize deep integration of multiple technologies and the unified achievement of both safety and real-time performance. Full article

In this paper, a Gaussian process-based model predictive control (GP-MPC) method is proposed, which aims to enhance the trajectory tracking performance of autonomous underwater vehicles (AUVs). This method can compensate for internal errors and external disturbances based on a limited amount of data. Firstly, numerical models of the AUV are presented. Then, the offline GP-MPC algorithm and online GP-MPC algorithm are presented and described. Meanwhile, the current disturbances and initial errors are also considered. The circular trajectory, L-shaped steering trajectory, and lemniscate trajectory are tracked to evaluate the trajectory tracking performances of different algorithms. Compared with proportional–integral–derivative (PID) and nominal MPC algorithms, the GP-MPC algorithms show reduced root mean square error (over 40%) and reduced maximum error (over 40%) in both position and yaw angle when performing different trajectory tracking tasks. Finally, real-time pool experiments are conducted to validate the implementation feasibility of the GP-corrected MPC framework on a physical AUV under surface three-degrees-of-freedom motion, while the online GP-MPC is evaluated through numerical simulations. Full article

As modern electrical grids increasingly incorporate renewable generation—specifically from wind and solar–thermal installations—they face heightened volatility and operational complexities, which severely complicate load frequency regulation. While fractional-order proportional-integral-derivative (FOPID) controllers are commonly employed for this purpose, their conventional formulations rely on fixed fractional parameters that cannot adapt to fluctuating network conditions. To address this limitation, the present study develops an adaptive FOPID (AFOPID) control architecture capable of real-time adjustment of fractional orders, thereby enhancing regulatory effectiveness. The Coot Optimization Algorithm (COA) is utilized to optimally determine the operational parameters of all controllers under investigation. The proposed strategy is validated on a simulated hybrid power system comprising wind generation, solar–thermal units, and physical nonlinearities including governor dead band and generation rate constraints. A comparative analysis is conducted across four distinct operating scenarios, benchmarking the COA-tuned AFOPID against conventional PI, PID, and standard FOPID controllers. Quantitative results demonstrate that the proposed COA-AFOPID configuration achieves superior performance, with improvements in settling time up to 46.06% and reductions in ITAE index up to 89.89% compared to traditional methods. These findings confirm the enhanced stability and robustness of the proposed approach for frequency regulation in sustainable energy networks. Full article

Maintaining precise liquid levels in interconnected tank systems is a critical requirement in many industrial processes; however, achieving reliable control remains challenging due to inherent nonlinearities and external disturbances. This paper presents a comparative analysis of three control strategies—sliding mode control (SMC), feedback linearization (FL), and proportional–integral–derivative (PID) control—applied to a nonlinear two-tank system. To address the practical limitation of unmeasured system states, a high-gain observer (HGO) is integrated into the control architecture to reconstruct unmeasured water levels. In addition, the controller and observer parameters are optimized using a hybrid genetic algorithm to balance tracking precision and control effort. Simulation results demonstrate that, although all three methods achieve acceptable setpoint tracking performance, the SMC-HGO configuration exhibits superior robustness. Specifically, it outperforms FL and PID in rejecting external disturbances and maintaining stability under significant parameter variations, such as changes in discharge coefficients. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

To address the limitations of traditional controllers for the considered six-degree-of-freedom multi-thruster underwater robot under strong nonlinearities and environmental disturbances, this paper proposes a particle swarm optimization–radial basis function–proportional–integral–derivative (PSO-RBF-PID) control algorithm. The proposed method combines the nonlinear identification capability of the RBF neural network, the global optimization capability of PSO, and the stable closed-loop structure of PID control, thereby enabling adaptive parameter tuning and disturbance compensation. Unlike existing PSO-PID- and RBF-based controllers, the proposed method combines offline global optimization and online adaptive gain tuning within a unified control framework. Although the framework is modular and can be extended to underwater robotic systems with different degrees of freedom by redefining the state vector, controller channels, and thrust allocation matrix, the present study validates the method through a six-degree-of-freedom multi-thruster underwater robot case study. Comparative simulations were conducted under the same model, disturbance conditions, sampling settings, and evaluation indices for six controllers: PID, cascade PID, fuzzy PID, FOPID, PSO-PID, and PSO-RBF-PID. For the considered 6-DOF multi-thruster underwater robot, PSO-RBF-PID achieved the best overall performance in steady-state error, settling time, overshoot, and IAE. This improvement is mainly attributed to the combination of PSO-based offline optimization and RBF-based online adaptive compensation. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

Background: While trefoil factor 3 (TFF3) has been linked to cardiovascular disease, its role in peripheral artery disease (PAD) remains largely unexplored. In this prospective study, we assessed three pre-selected circulating biomarkers and found that TFF3 demonstrated the strongest association with the presence of PAD. Building on this finding, we integrated plasma TFF3 concentrations with clinical characteristics to construct predictive models aimed at identifying individuals with PAD and estimating their risk of major adverse limb events (MALE) over a two-year follow-up period. Methods: A total of 476 individuals were prospectively recruited, including 312 patients with PAD and 164 controls without PAD. At study entry, circulating concentrations of TFF3, oncostatin M (OSM), and brain-derived neurotrophic factor (BDNF) were quantified, and all participants were subsequently monitored for a two-year period. The primary endpoint was the occurrence of MALE within two years, comprising acute limb ischemia, major amputation, or lower extremity revascularization by either open surgical or endovascular approaches. PAD diagnosis served as the secondary outcome and was established by an ankle–brachial index (ABI) ≤ 0.9 or toe–brachial index (TBI) ≤ 0.67 in the presence of reduced or absent pedal pulses. For predictive model development, the cohort was randomly divided into training (70%) and testing (30%) sets. A random forest algorithm incorporating clinical variables and plasma TFF3 levels was developed and optimized using 10-fold cross-validation. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUROC). For prognostic evaluation, patients were classified into low- and high-risk groups based on the optimal ROC-derived probability threshold of 0.60, and MALE-free survival between groups was assessed using Cox proportional hazards regression. Results: Among the three candidate biomarkers evaluated, only TFF3 demonstrated a significant association with PAD. Patients with PAD exhibited higher circulating TFF3 concentrations than those without PAD (7.27 ± 3.36 vs. 5.89 ± 2.67 pg/mL; p < 0.001), whereas OSM and BDNF showed no significant differences between groups. Over the two-year follow-up period, MALE occurred in 28 patients (9%). Predictive models combining plasma TFF3 measurements with clinical variables achieved strong performance for both PAD detection and 2-year MALE risk estimation, yielding AUROCs of 0.79 and 0.85, respectively. Furthermore, patients classified as high risk by the model experienced a significantly increased hazard of MALE during follow-up (HR 1.12, 95% CI 1.10–1.19; p = 0.003). Variable importance analysis revealed that TFF3 was the most influential predictor of MALE, followed by age and smoking history. Conclusions: Combining plasma TFF3 levels with readily available clinical characteristics enabled the development of a predictive model with good discriminatory ability for both PAD diagnosis and estimation of 2-year MALE risk. Such an approach may enhance risk stratification by identifying patients at elevated risk earlier in their disease course, thereby informing decisions related to vascular testing, referral for specialist evaluation, and implementation of targeted treatment strategies. Full article

A nonlinear backstepping control framework is developed for autonomous landing of a quadrotor on a wave-excited marine platform. This study addresses the underactuated nature of the aerial vehicle and the strong coupling between translational and rotational dynamics, ensuring stable trajectory tracking under sea-induced disturbances. Reference trajectories are generated through physically grounded Pierson–Moskowitz (PM) and modified Pierson–Moskowitz (MPM) wave spectra, enabling realistic modeling of vertical heave motion, while horizontal position and yaw are defined through harmonic components adapted to the sea-state regime. The controller is designed through a seven-step recursive backstepping procedure, with Lyapunov functions guaranteeing asymptotic stability of the tracking errors for the regulated outputs. A modular MATLAB simulation platform is implemented, integrating the full six-DOF quadrotor dynamics, the control algorithm, and spectral reference generation. Numerical simulations demonstrate that the Lyapunov function derivatives remain negative over the entire simulation horizon, confirming asymptotic convergence. Comparative results with a tuned PID (proportional integral derivative) controller indicate superior tracking performance and damping and reduced amplitude and phase errors for the backstepping approach, especially under MPM-based trajectories representing rough sea states. The proposed framework establishes a reliable basis for adaptive extensions and future hardware-in-the-loop validation of autonomous landing on moving marine platforms. Full article