Search Results (316)

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

This paper focuses on the control problems of a multi-modal underwater robot, which is designed mainly for the task of detecting the working environment in deep-sea mining. To tackle model uncertainty and external disturbances, an improved linear active disturbance rejection control scheme based on sliding mode control is proposed (SM-ADRC). Firstly, to reduce overshoot, a piecewise $fhan$ function is introduced into the tracking differentiator (TD). This design retains the system’s fast nonlinear tracking characteristics outside the boundary layer while leveraging linear damping within it to achieve effective overshoot suppression. Secondly, two key enhancements are made to the SMC: an integral sliding surface is designed to improve steady-state accuracy, and a saturation function replaces the sign function to suppress high-frequency chattering. Furthermore, the SMC integrates the total disturbance estimate from the linear extended state observer (LESO) for feedforward compensation. Finally, the simulation experiment verification is completed. The simulation results show that the SM-ADRC scheme significantly improves the dynamic response and disturbance suppression ability of the system and simultaneously suppresses the chattering problem of SMC. Full article

This study tackles the challenges associated with permanent magnet synchronous motor (PMSM) position control under complex operating conditions—characterized by strong coupling, nonlinearity, and time-varying parameters—which often lead to slow response, low control accuracy, and weak disturbance rejection capability. A high-performance control system is developed based on an active disturbance rejection controller (ADRC), with three key improvements proposed. Firstly, a modified nonlinear function is designed to suppress chattering. Secondly, a delay compensation module is integrated to synchronize the input signals of the extended state observer (ESO). Finally, an automated parameter tuning method is introduced using the Newton-Raphson optimization algorithm. Comparative simulations are conducted to validate the effectiveness of the proposed system, demonstrating its advantages of rapid response, minimal overshoot, and enhanced disturbance rejection capability. For the proposed strategy, the maximum position tracking error is 0.1 rad, the adjustment time is 0.15 s, the dynamic speed drop is 0.025 rad, and the recovery time is 0.15 s—all comprehensive performance indicators outperform those of other control strategies. Additionally, automated parameter tuning eliminates the need for manual adjustments, reduces operational complexity, and improves tuning accuracy, thereby significantly advancing the position control performance of PMSMs. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

Hybrid underwater gliders (HUGs) combine buoyancy-driven gliding with propeller-assisted propulsion, offering extended endurance and enhanced mobility for complex underwater missions. However, precise depth control remains challenging due to system uncertainties, environmental disturbances, and inadequate adaptability of conventional control methods. This study proposes a novel optimized line-of-sight active disturbance rejection control (OLOS-ADRC) strategy for HUG depth tracking in the vertical plane. First, an Optimized Line-of-Sight (OLOS) guidance dynamically adjusts the look-ahead distance based on real-time cross-track error and velocity, mitigating error accumulation during path following. Second, a Tangent Sigmoid-based Tracking Differentiator (TSTD) enhances the disturbance estimation capability of the Extended State Observer (ESO) within the Active Disturbance Rejection Control (ADRC) framework, improving robustness against unmodeled dynamics and ocean currents. As a critical step before costly sea trials, this study establishes a high-fidelity simulation environment to validate the proposed method. The comparative experiments under gliding and hybrid propulsion modes demonstrated that OLOS-ADRC has significant advantages: the root mean square error (RMSE) for depth tracking was reduced by 83% compared to traditional ADRC, the root mean square error for pitch angle was decreased by 32%, and the stabilization time was shortened by 14%. This method effectively handles ocean current interference through real-time disturbance compensation, providing a reliable solution for high-precision HUG motion control. The simulation results provide a convincing foundation for future field validation in oceanic environments. Despite these improvements, the study is limited to vertical plane control and simulations; future work will involve full ocean trials and 3D path tracking. Full article

Diabetic retinopathy (DR) causes visual impairment and blindness in millions of diabetic patients globally. Fundus image-based Automatic Diabetic Retinopathy Classifiers (ADRCs) can ensure regular retina checkups for many diabetic patients and reduce the burden on the limited number of retina experts by referring only those patients who require their attention. Over the last decade, numerous deep neural network-based algorithms have been proposed for ADRCs to distinguish the severity levels of DR. However, it has not been investigated whether DNN-based ADRCs consider the same criteria as human retina professionals (HRPs), i.e., whether they follow the same grading scale when making decisions about the severity level of DR, which may put the reliability of ADRCs into question. In this study, we investigated this issue by experimenting on publicly available datasets using MobileNet-based ADRCs and analyzing the output of the ADRCs using two eXplainable artificial intelligence (XAI) techniques named Gradient-weighted Class Activation Map (Grad-CAM) and Integrated Gradients (IG). Full article

To address heading instability in electric vertical take-off and landing (eVTOL) aircraft at low speeds and large pitch angles, a rotational speed feedback compensation control scheme based on Active Disturbance Rejection Control (ADRC) is proposed for variable-pitch propellers. This scheme integrates propeller speed into the heading control inner loop and employs a state observer to process the measured speed. Simulation results demonstrate that under dynamic propeller speed variations of 0.5%, 1%, and 2%, the proposed compensation scheme reduces yaw angle oscillation amplitudes by 22.2%, 30.6%, and 37.8%, and yaw angular velocity fluctuations by 32.5%, 43.4%, and 33.3%, respectively, compared to a basic speed feedback scheme, showcasing significantly superior robustness. Experimental bench tests further validate that the proposed strategy enhances overall propeller force efficiency from 2.479 kg/kW to 3.05 kg/kW at 120 km/h cruise, resulting in a power saving of 0.48 kW and extending the cruising range by 8.5 km. The stability and energy efficiency of the proposed method are rigorously validated through both simulation and experimental testing. Full article

To address the problems of random noise interference, inadequate disturbance estimation and compensation, and the difficulty in controller parameter tuning in speed tracking control of high-speed trains, an improved Active Disturbance Rejection Control (ADRC) strategy combined with a Sobol-based Black Widow Optimization (SBWO) algorithm is proposed. An improved Tracking Differentiator (TD) is adopted by integrating a novel optimal control synthesis function with a phase compensator to suppress input noise and ensure a smooth transition process. A novel Extended State Observer (ESO) using a nonlinear saturation function is designed to improve the observation accuracy and decrease chattering. An enhanced Nonlinear State Error Feedback (NLSEF) law that incorporates an error integral and adaptive parameter update laws is developed to reduce steady-state error and achieve self-tuned proportional and derivative gains. A feedforward compensation term is added to provide real-time dynamic compensation for ESO estimation errors. Finally, an enhanced Black Widow Optimization (BWO) algorithm, which initializes its population with Sobol sequences to improve its global search capability, is employed for parameter optimization. The simulation results demonstrate that compared with the control methods based on Proportional–Integral–Derivative (PID) control and conventional ADRC, the proposed strategy achieves higher steady-state tracking accuracy, better adaptability to dynamic operating conditions, stronger anti-disturbance ability, and more precise stopping precision. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

Sliding mode control (SMC) provides robustness and disturbance rejection in permanent magnet synchronous motor (PMSM) control but faces the challenge of speed degradation during sudden load disturbance changes without a load observer. This paper proposes a backpropagation neural network-adjusted improved SMC (BPNN-ISMC). A simplified PMSM model is established by ignoring the disturbance term. An improved arrival law is developed by optimizing the constant-speed approach term of the traditional exponential arrival law and embedding an adaptive term. A BPNN is designed with performance metrics including speed error, its derivative, and maximum error to improve training efficiency. Speed/position estimation combines a sliding mode observer with an extended Kalman filter to suppress jitter. Simulation results demonstrate the significant advantages of the BPNN-ISMC method: in set-point control, overshoot suppression is evident, and the relative error during the stable phase after sudden load disturbance increases is reduced by 93.62% compared to the ISMC method and by 99.80% compared to the SMC method. Compared to the ADRC method, although the steady-state errors are the same, the BPNN-ISMC method exhibits smaller speed fluctuations during sudden changes. In servo control, the root mean square error of speed tracking is reduced by 18.83% compared to the ISMC method, by 89.70% compared to the SMC method, and by 37.14% compared to the ADRC method. This confirms the dynamic performance improvement achieved through adaptive adjustment of neural network parameters. Full article

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system and ADRC model of the PMSM are established. Then, a nonlinear function, ifal, is introduced to improve the performance of the speed-loop ADRC. Meanwhile, an active disturbance rejection controller is also introduced into the current loop to suppress current disturbances. To address the challenge of tuning multiple ADRC parameters, an opposition-based learning hybrid optimization algorithm (OBLHOA) is developed. This algorithm integrates chaotic mapping for population initialization and employs opposition-based learning to enhance global search capability. The proposed OBLHOA is utilized to optimize the speed-loop ADRC parameters, thereby achieving high-precision speed control of the PMSM system. Its optimization performance is validated on 12 benchmark functions from the IEEE CEC2022 test suite, demonstrating superior convergence speed and solution accuracy compared to conventional heuristic algorithms. The proposed strategy achieves superior speed regulation accuracy and reliability under complex operating conditions when deployed on high-performance processors, but its effectiveness may diminish on resource-limited hardware. Moreover, simulation results show that the DLADRC-OBLHOA control strategy outperforms PI control, traditional ADRC, and ADRC-$ifal$ in terms of tracking accuracy and disturbance rejection capability. Full article

To enable safe cooperative flight of multi-UAV formations in urban 3-D airspace with wind-field disturbances, we develop an integrated planning-control framework.The planning layer uses an APF-guided RRT* with continuous collision prediction and explicit velocity/acceleration limits, and compensates wind online.The control layer adopts a dual-loop MA-AADRC-SMC design. An adaptive ESO estimates disturbances for feed-forward cancellation, and an SMC term improves robustness and tracking accuracy. By coupling the planned trajectory with speed-weighted repulsive fields, the framework coordinates path and attitude in closed loop, enabling collision-free and overshoot-free formation flight in wind and clutter. Simulations show higher tracking accuracy and better formation stability than ADRC, PID and SMC. A Lyapunov analysis proves uniform boundedness and asymptotic stability. The framework is scalable to applications such as disaster assessment and urban air transport. Full article

This study presents the configuration design and attitude control of a moving-mass coaxial dual-rotor UAV (MMCDRUAV) for indoor applications. Compared with existing configurations, the proposed configuration avoids additional actuation mass and improves the control authority. Based on these improvements, a promising micro UAV platform with a high payload ability for agile indoor flight could be developed. Ground validation tests demonstrated its maneuverability, as provided by a moving-mass control (MMC) module requiring only the repositioning of existing components (e.g., battery packs) as movable masses. For trajectory tracking, an adaptive backstepping active disturbance rejection controller (ADRC) is proposed. The architecture integrates extended-state observers (ESOs) for disturbance estimation, parameter-adaptation laws for uncertainty compensation, and auxiliary systems to address control saturation. Lyapunov stability analysis proved the existence of uniformly ultimately bounded (UUB) closed-loop tracking errors. The results of the ground verification experiment confirmed enhanced tracking performance under real-world disturbances. Full article

To address the challenge of low trajectory tracking accuracy for underactuated unmanned underwater vehicles (UUVs) under external disturbances, this study proposes a method integrating backstepping control with improved active disturbance rejection control (IADRC), which enhances high-precision trajectory tracking performance for UUV systems. Firstly, a five-degree-of-freedom dynamic model is established according to the symmetrical structure characteristics of an underactuated UUV, and virtual control inputs are designed using the backstepping method to address the underactuated characteristics. To improve convergence speed and tracking accuracy, a nonsingular terminal sliding mode control (NTSMC) is incorporated into the ADRC framework. Additionally, a parameter-adaptive tracking differentiator (PATD) is developed to mitigate the “differential explosion” problem inherent in backstepping virtual control inputs. A model-assisted extended state observer (ESO) is also designed to accurately estimate system disturbances. Stability analysis, grounded in Lyapunov theory, rigorously proves that all tracking errors converge asymptotically to a small bounded neighborhood of the origin. Simulation results demonstrate the effectiveness and superiority of the proposed control strategy. Full article

In this paper, we propose a dual-loop Active Disturbance Rejection Control (ADRC) strategy for gust load alleviation in flexible aircraft. By decoupling the control of modal and normal accelerations and spatially allocating control surfaces, the method effectively resolves signal interference. Simulation results show that compared to the uncontrolled case, the ADRC controller reduces the wing root bending moment peak by 38%, the normal load factor peak by 32%, and the pitch angle fluctuation by 38%. Robustness tests under actuator delays (4 Δt and 8 Δt) and gain perturbations (−50% and +100%) further confirm that the system maintains time-domain stability and effective load mitigation across varying conditions. These results demonstrate that the proposed ADRC scheme not only improves load suppression but also offers strong robustness against parameter uncertainty, providing theoretical and practical support for next-generation active control systems in aeroelastic environments. Full article