Search Results (132)

Micro quadrotor unmanned aerial vehicles (UAVs) are highly sensitive to external disturbances and model uncertainties because of their small mass, low moment of inertia, and limited onboard computational resources. To improve the disturbance rejection and trajectory tracking performance of micro quadrotor UAVs, this paper proposes an event-triggered extended state observer (ET-ESO)-based prescribed-time funnel control (PTFC) method. First, a control-oriented dynamic model of the micro quadrotor is established, in which wind disturbances, unmodeled aerodynamic effects, damping uncertainties, and parameter perturbations are represented as lumped disturbances in the translational and rotational subsystems. Then, two event-triggered ESOs are designed to estimate the lumped disturbances of the velocity and angular velocity channels. Compared with conventional continuously sampled ESO schemes, the proposed event-triggered mechanism reduces the frequency of sensor-to-controller information transmission while preserving disturbance estimation capability. Furthermore, a prescribed-time funnel control law is developed to constrain the position and attitude tracking errors within predefined performance boundaries and ensure convergence to the desired accuracy region within a user-specified time. Lyapunov-based stability analysis is provided to prove the boundedness of all closed-loop signals and the validity of the prescribed funnel constraints. Finally, MATLAB/Simulink simulations based on the Parrot Mambo mini-drone parameters are conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed controller achieves robust trajectory tracking, effective disturbance compensation, improved transient performance, and reduced control update frequency. Full article

During actual vehicle operation, working conditions are highly complex, involving both body roll induced by steering centrifugal force and attitude fluctuations caused by random road irregularities or sudden lateral wind disturbances. By optimizing the control of the active stabilizer bar (ASB), its torque compensation capability can be more effectively utilized, thereby improving vehicle ride quality and handling stability under extreme conditions. This paper first establishes a vehicle roll model with a passive stabilizer bar. Then, an active disturbance rejection control (ADRC) controller, a linear active disturbance rejection control (LADRC) controller, and a fuzzy proportional–integral and proportional–derivative (PI-PD) controller are designed and verified through simulation. The results show that all three active control methods improve roll stability compared with the passive system, and the ADRC controller achieves better control performance than the fuzzy PI-PD and LADRC controllers. Furthermore, a control strategy for the active stabilizer bar model is developed based on the deep deterministic policy gradient (DDPG) algorithm. The simulation results show that, using deep reinforcement learning for feedforward optimization, the fuzzy PI-PD, LADRC, and ADRC control methods reduce the body roll angle by 3.8%, 27.1%, and 25.0%, respectively. The front-axle anti-roll moments are reduced by 13.4%, 14.0%, and 16.5%, respectively, while the rear-axle anti-roll moments are reduced by 14.8%, 13.4%, and 14.5%, respectively. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

This paper investigates the flight-control problem of a vector–rotor UAV (VR-UAV) for orchard crop-inspection tasks, where wind acts as the dominant external disturbance source. In such tasks, the UAV is required to maintain position while adjusting its attitude for flexible sensor pointing. For a conventional quadrotor UAV (QUAV), however, position and attitude are strongly coupled because the thrust directions are fixed relative to the fuselage, which limits its ability to perform stable hovering and directional sensing simultaneously. Although gimbal-based solutions can provide sensing-direction adjustment, they may become less suitable for wind-affected low-altitude inspection tasks involving large, elongated, or multi-sensor payloads, due to the added mass, inertia, structural compliance, and vibration sensitivity introduced by the additional mechanism. To address these limitations, this paper proposes a compact VR-UAV platform together with an adaptive robust constraint-following control (ARCFC) method. By incorporating tilting motors for thrust-vector adjustment, the proposed VR-UAV enables decoupled regulation of position and attitude, thereby improving fixed-point hovering capability and flexible sensor pointing. From the control perspective, the thrust-vectoring mechanism introduces strongly nonlinear coupled dynamics, while wind-induced disturbances and modeling uncertainties further complicate the control problem. To address these challenges, a constraint-following control framework is developed to handle the nonlinear dynamics, and an adaptive robust compensation mechanism is introduced to estimate the uncertainty bound online and compensate for unknown but bounded disturbances. The closed-loop stability and robustness of the proposed method are rigorously established by theoretical analysis. Comparative simulation results demonstrate that, relative to a conventional QUAV, the proposed VR-UAV with ARCFC achieves superior flight stability, stronger wind-disturbance rejection, and better trajectory-tracking performance in wind-affected orchard inspection scenarios. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

Wind power uncertainty can significantly deteriorate the frequency regulation performance and robustness of load frequency control (LFC) systems, particularly in the presence of communication delays. However, most existing studies rely on simplified wind power fluctuation models, which cannot adequately capture the segmented and stochastic characteristics of wind speed variations. As a result, the resulting robustness analysis may deviate from practical operating conditions, leading to controller designs that are less reliable and less effective in real-world scenarios. To address this issue, this paper develops a robust controller co-design framework for delayed LFC systems under wind power uncertainty. First, a probabilistic wind power model with wind speed segmentation characteristics is established, and electric vehicles are incorporated into frequency regulation to construct a multi-area delayed LFC model. Then, a robust performance index is introduced to quantify disturbance rejection capability, and a genetic algorithm–particle swarm optimization-based collaborative optimization strategy is employed to determine controller parameters efficiently. Simulation results on both single-area and two-area LFC systems demonstrate that the proposed method achieves superior frequency regulation performance and stronger robustness against wind disturbances and time delays compared with designs that neglect wind uncertainty. Quantitatively, compared with controllers designed based on simplified wind power modeling, the proposed design framework reduces the normalized integral of time multiplied absolute value of the error (ITAE), integral of squared error (ISE), and integral of absolute error (IAE) indices by approximately 17.4% and 9% on average in the single-area and two-area cases, respectively. Full article

Quadrotor unmanned aerial vehicles (UAVs), commonly known as quadcopters, pose a fundamental control challenge: they are underactuated, open-loop unstable, strongly coupled across six degrees of freedom, and highly susceptible to aerodynamic disturbances and payload uncertainty. Classical model-based controllers degrade significantly when the vehicle mass shifts due to battery discharge or payload pickup, or when wind gusts produce forces comparable to the available thrust margin—an especially acute problem for nano-scale quadcopters. This paper proposes an active disturbance rejection control (ADRC) to address these challenges. Rather than attempting to model every source of uncertainty, we employ an extended state observer (ESO) to estimate a single total disturbance signal—comprising unmodeled dynamics, parametric errors, nonlinear coupling, and external disturbances—in real time from sensor measurements alone, and cancel it before applying a simple feedback law. Building on this principle, we derive a cascaded linear ADRC (LADRC) architecture that governs all six degrees of freedom of the quadcopter and formulate the quadrotor-specific total-disturbance structure for each control channel. Simulations demonstrate that the proposed controller maintains small, bounded position-tracking RMS errors under 30% mass uncertainty combined with a sustained lateral wind-gust disturbance, while delivering the correct hover thrust automatically without prior knowledge of the true mass and without integrator wind-up. Full article

High penetration of converter-based wind generation reduces system inertia. It poses challenges to frequency stability in modern distribution networks, particularly in doubly fed induction generator (DFIG)-based wind-energy-conversion systems (WECSs), where frequency regulation is coupled with point-of-common-coupling (PCC) voltage and power factor (PF) dynamics. This study presents a multi-objective comparative evaluation of proportional–integral (PI), proportional–integral–derivative (PID), fractional-order PID (FOPID), and adaptive neuro-fuzzy inference system (ANFIS) controllers for a DFIG-based WECS connected to a radial distribution feeder. Controller parameters are tuned using multi-objective optimisation, considering frequency deviation, overshoot, settling time, disturbance robustness, control smoothness, and computational cost, while maintaining PCC voltage and PF within acceptable limits. MATLAB/Simulink simulations are conducted under turbulent wind conditions, load variations, voltage disturbances, and measurement noise. The results indicate that conventional PI and PID controllers exhibit limited performance under low-inertia conditions, whereas FOPID improves damping and voltage/PF behaviour. ANFIS achieves the best overall performance, providing reduced frequency deviation, faster settling time (below 3 s), improved disturbance rejection, and significantly lower integral absolute error (up to ~90%) compared to PI control. These findings offer practical guidance for selecting and tuning controllers to enhance frequency-centric stability in wind-integrated distribution networks. Full article

Bio-inspired flapping-wing micro air vehicles (FWMAVs) are a research hotspot in micro air vehicles due to their high maneuverability and hovering capabilities. Accurate motion damping modeling is a prerequisite for their attitude disturbance rejection and control law design. Addressing the key issues in existing research—namely, the low computational efficiency of high-fidelity flexible-wing aerodynamic simulations and the inability of efficient rigid-wing assumptions to capture dynamic deformation of flexible wings—this paper investigates motion damping modeling for FWMAVs and its application to lateral flight stability analysis. First, an aerodynamic damping model under lateral motion parameters is established by approximating the flexible-wing surface using the spatial topology of the spar and veins. Second, numerical simulations of the flapping trajectory and motion damping are conducted. Subsequently, the validity and reliability of the model are verified through wind tunnel and turntable experiments. Finally, leveraging this model, lateral flight dynamics equations are derived to perform lateral stability analysis. The results effectively address the gap in assessing flapping-induced aerodynamic damping for flexible wings, providing an accurate analytical damping model, an efficient simulation framework, and an effective open-loop dynamics assessment method for the rapid design iteration and control algorithm development of FWMAVs. Full article

Self-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. However, its performance strongly depends on the robustness of the control algorithm against harmonics, load imbalance, and sensor-induced measurement errors such as DC offset, which degrade reference current generation. This study proposes an Advanced Dual Fourth-Order Generalized Integrator (ADFOGI)-based control algorithm to improve voltage and frequency regulation of SEIG–DSTATCOM systems under such adverse conditions. The proposed method inherently rejects DC offset components and enables accurate reference current generation even under severe harmonic distortion, load imbalance, and transient disturbances. The effectiveness of the approach is validated on an OPAL-RT real-time platform under three scenarios: nonlinear load, unbalanced nonlinear load, and one-phase open-circuit condition, where DC offset is intentionally introduced to emulate sensor errors. Under the most severe case, where load current THD reaches 16.23%, SEIG current THD is reduced to 3.71% and voltage THD to 1.66%. In all scenarios, harmonic levels remain below the IEEE-519-2022 limit of 5%, confirming the robustness and effectiveness of the proposed control strategy. Full article

This paper proposes an adaptive augmented anti-disturbance load relief control scheme for a solid launch vehicle. It can effectively satisfy the composite control requirements including high-precision attitude control, resistance to elastic frequency deviations, sudden wind disturbances, and active load relief. Firstly, the dynamic model of the elastic solid launch vehicle was established and subjected to small-perturbation linearization. Based on the state-space approach, the open-loop transfer function of the system was derived, and a basic PD controller with correction networks was presented. Subsequently, an adaptive augmented control law was designed to achieve adaptive variation in open-loop gain. Furthermore, a load relief control law was designed to address the launch vehicle’s need for load mitigation during the ascent phase through high-wind regions. Simultaneously, to further enhance disturbance rejection capability, a linear extended state observer was developed. Finally, frequency-domain methods and sinusoidal function analysis were applied to the four designed modules to evaluate the system’s stability margins, and the overall stability margin of the whole control system was calculated. Comprehensive time-domain simulation results and frequency-domain analysis examples demonstrate the effectiveness of the proposed method, which offers a novel solution for launch vehicle ascent control and facilitates meeting multi-constraint control requirements. Full article

Formation control of unmanned surface vehicles (USVs) in complex marine environments is required to contend with strongly coupled, high-dimensional disturbances. A Multi-Agent Active Disturbance Rejection Control (MAADRC) framework is developed for this purpose. The design centers on a distributed extended state observer (DESO) coupled with a dual-channel feedback structure—NEFL-GCO and LGL-FC—that collectively maintains formation geometry. Three main ideas underpin the approach. First, a bandwidth-efficient distributed observation scheme enables agents to share disturbance estimates while using substantially less communication bandwidth. Second, an adaptive consensus compensation mechanism accommodates parameter variations as formations evolve. Third, a formation-compatible obstacle avoidance algorithm enhances reliability in congested waters. To evaluate the control structure and optimize its parameters, a multi-agent reinforcement learning (MARL) method—specifically Multi-Agent Proximal Policy Optimization (MAPPO)—is employed. The MARL agent tunes two critical parameters: observer bandwidth and nonlinear feedback gain, thereby establishing a performance baseline. After ten million training steps, the MAPPO-optimized MAADRC achieves a tracking root-mean-square error (RMSE) of 1.18 m. This value lies within 3% of the manually tuned result of 1.21 m, indicating that the bandwidth parameterization is near-optimal. Extensive simulations incorporating realistic wind, wave and current disturbances demonstrate a dynamic obstacle avoidance success rate maintaining an expected level, alongside consistently low formation tracking errors. Collectively, these findings confirm the resilience and practical utility of the proposed framework in demanding maritime settings. Full article

Quadrotor unmanned aerial vehicles (UAVs) operating in complex and dynamic environments, especially when subjected to unknown disturbances such as wind, can experience significant degradation in the stability of trajectory tracking control. Current research on UAV control has proposed algorithms that exhibit good disturbance rejection capabilities for small and weak disturbances, but their effectiveness decreases significantly as the disturbance magnitude increases. To address this issue, this paper proposes a hybrid control strategy that combines a Radial Basis Function Neural Network (RBFNN) with Integral Terminal Sliding Mode Control (ITSMC). The RBFNN is designed as an online disturbance observer, capable of estimating and compensating external disturbance forces and torques in real time, with an adaptive weight law. The ITSMC utilizes an integral term to eliminate steady-state errors and a terminal sliding mode term to achieve finite-time convergence of tracking errors. Simulation results demonstrate that the proposed controller maintains high-precision trajectory tracking and attitude control performance under various disturbance conditions, exhibiting strong robustness and anti-disturbance capability, and outperforms other controllers in overall performance. Full article

The increasing penetration of Doubly Fed Induction Generator (DFIG)-based wind energy systems raises major concerns regarding voltage stability and Fault Ride-Through (FRT) capability under grid disturbances and wind speed variations. This paper proposes a coordinated control framework for a grid-connected DFIG system, where a Static Synchronous Compensator (STATCOM) based on discrete-time deadbeat current control is integrated with a Supercapacitor Energy Storage System (SCES) connected to the DC link through a bidirectional DC-DC converter governed by cascaded Active Disturbance Rejection Control (ADRC). The deadbeat-controlled STATCOM provides fast reactive current injection for voltage support during sag and swell events, while the cascaded ADRC enhances DC-link voltage regulation and suppresses rotor-speed oscillations. Comprehensive MATLAB/Simulink simulations are carried out under variable wind speed and severe grid disturbances up to 80% voltage sag and 50% voltage swell. For voltage regulation, the proposed method is compared with SVC and PI-based STATCOM. In addition, SCES control performance is evaluated by comparing PI, single ADRC, and cascaded ADRC in terms of DC-link voltage overshoot, undershoot, and ripple. The results show clear improvements in voltage response and transient performance. Under a 20% voltage sag, the proposed deadbeat-controlled STATCOM significantly improves the dynamic response, where the undershoot is reduced from 0.125 p.u. (with SVC) to 0.04 p.u., and the settling time is shortened from 0.04 s to 0.025 s. Under a severe 80% sag, the overshoot is limited to 0.02 p.u., compared with 0.13 p.u. for the SVC and 0.15 p.u. for the PI-based STATCOM. Similarly, under a 50% voltage swell, the overshoot is reduced to 0.20 p.u., compared with 0.46 p.u. for the SVC and 0.27 p.u. for the PI-based STATCOM. Regarding the DC-link performance under 80% sag, the proposed cascaded ADRC-based SCES limits the overshoot and undershoot to 6 V and 2 V, respectively, compared with 39 V and 32 V for the PI-based SCES. These results confirm the superior damping, disturbance rejection, and FRT enhancement achieved by the proposed strategy. Full article

With the integration of renewable energy into traction power supply systems at a high proportion and penetration level, the intermittency and randomness of renewable energy output significantly intensify the fluctuation characteristics of traction loads, posing severe challenges to the stable operation and precise dispatch of the system. To effectively address the dynamic tracking and anti-disturbance issues arising from the dual uncertainties of source and load, this paper proposes a dual-timescale two-layer optimization dispatch strategy based on Model Predictive Control (MPC). In the upper-layer optimization, with the objective of optimal system economic operation, a multi-step rolling optimization method is adopted to formulate a long-timescale baseline dispatch plan, fully considering the temporal correlation of photovoltaic and wind power outputs and the periodic characteristics of traction loads. In the lower-layer optimization, aimed at smoothing power fluctuations and correcting prediction deviations, the technical advantages of supercapacitors—high power density and fast response—are utilized to perform real-time tracking and dynamic compensation of the upper-layer baseline plan. This effectively reduces the impact of prediction errors on control accuracy, achieves smooth control of tie-line power, and enhances overall system stability. Case study results based on an actual railway traction power supply system demonstrate that the proposed method can fully leverage the coordinated and complementary characteristics of the hybrid energy storage system, effectively suppress power fluctuations from renewable energy output and traction loads, and achieve economic operation objectives while ensuring system disturbance rejection performance, thereby validating the effectiveness and practicality of the strategy. Full article