Search Results (298)

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

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article

In the context of the mining industry, the belt conveyor is a critical piece of equipment. The motor constitutes the primary component of the belt conveyor apparatus, and its stable and accurate operation can significantly influence the performance of the belt conveyor apparatus. This paper introduces an integrated control approach combining vector control methodology with active disturbance rejection control (ADRC) for velocity regulation and model predictive control (MPC) for current tracking. The ADRC framework actively compensates for load disturbances and parameter variations during speed control, while MPC achieves precise current regulation with minimal tracking error. Validation involved comprehensive MATLAB/Simulink R2024a simulations modeling PMSM behavior under mining-specific operating conditions. The results demonstrate substantial improvements in dynamic response characteristics and disturbance rejection capabilities compared to conventional control strategies. The proposed methodology effectively addresses critical challenges in mining conveyor applications, enhancing operational reliability and system longevity. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

Current treatment strategies for partial tendon tears often lack the capacity to promote true tissue regeneration and improve long-term clinical outcomes. This study tested the hypothesis that treatment of a partial defect in the rabbit common calcaneus tendon (CCT) with uncultured, unmodified, autologous, adipose-derived regenerative cells (UA-ADRCs) enables regenerative healing without scar formation. A full-thickness, 3 mm defect was produced in the midsubstance of the right gastrocnemius tendon, a component of the CCT, in adult female New Zealand white rabbits. Animals received either an injection of 28.3 × 10⁶ UA-ADRCs in 0.5 mL Ringer’s lactated solution (RLS) or saline, or RLS or saline alone as sham treatment. Tendons were analyzed 4 or 12 weeks post-treatment using histology, immunohistochemistry and non-destructive biomechanical testing. UA-ADRC-treated tendons showed newly formed connective tissue consistent with tendon regeneration, whereas sham-treated tendons developed scar tissue. Biomechanical testing showed significantly higher percent relaxation in UA-ADRC-treated tendons compared to sham controls (p < 0.05), indicating greater viscoelasticity characteristic of healthy or well-integrated tissue. Together, these findings suggest that UA-ADRC therapy may provide a regenerative, structure-modifying treatment for partial tendon tears. Full article

A scheme of load frequency control (LFC) is proposed based on the deep deterministic policy gradient (DDPG) and active disturbance rejection control (ADRC) for multi-region interconnected power systems considering the renewable energy sources (RESs) and energy storage (ES). The dynamic models of multi-region interconnected power systems are analyzed, which provides a basis for the subsequent RES access. Superconducting magnetic energy storage (SMES) and capacitor energy storage (CES) are adopted due to their rapid response capabilities and fast charge–discharge characteristics. To stabilize the frequency fluctuation, a first-order ADRC is designed, utilizing the anti-perturbation estimation capability of the first-order ADRC to achieve effective control. In addition, the system states are estimated using a linear expansion state observer. Based on the output of the observer, the appropriate feedback control law is selected. The DDPG-ADRC parameter optimization model is constructed to adaptively adjust the control parameters of ADRC based on the target frequency deviation and power deviation. The actor and critic networks are continuously updated according to the actual system response to ensure stable system operation. Finally, the experiment demonstrated that the proposed method outperforms traditional methods across all performance indicators, particularly excelling in reducing adjustment time (45.8% decrease) and overshoot (60% reduction). Full article

This paper presents a composite disturbance-tolerant control framework for quadrotor unmanned aerial vehicles (UAVs). By constructing an enhanced dynamic model that incorporates parameter uncertainties, external disturbances, and actuator faults and considering the inherent underactuated and highly coupled characteristics of the UAV, a novel robust adaptive sliding mode controller (RASMC) is designed. The controller adopts a hierarchical adaptive mechanism and utilizes a dual-loop composite adaptive law to achieve the online estimation of system parameters and fault information. Using the Lyapunov method, the asymptotic stability of the closed-loop system is rigorously proven. Simulation results demonstrate that, under the combined effects of external disturbances and actuator faults, the RASMC effectively suppresses position errors (<0.05 m) and attitude errors (<0.02 radians), significantly outperforming traditional ADRC and LQR control methods. Further analysis shows that the proposed adaptive law enables the precise online estimation of aerodynamic coefficients and disturbance boundaries during actual flights, with estimation errors controlled within ±10%. Moreover, compared to ADRC and LQR, RASMC reduces the settling time by more than 50% and the tracking overshoot by over 70% while using the ($tanh(·)$) approximation to eliminate chattering. Prototype experiments validate the fact that the method achieves centimeter-level trajectory tracking under real uncertainties, demonstrating the superior performance and robustness of the control framework in complex flight missions. Full article

Altitude test facilities for aero-engines employ multi-chamber, multi-valve intake systems that require effective decoupling and strong disturbance rejection during transient tests. This paper proposes a coordinated active disturbance rejection control (ADRC) scheme based on external penalty functions. The chamber pressure safety limit is formulated as an inequality-constrained optimization problem, and an exponential penalty together with a gradient based algorithm is designed for dynamic constraint relaxation, with guaranteed global convergence. A coordination term is then integrated into a distributed ADRC framework to yield a multi-valve coordinated ADRC controller, whose asymptotic stability is established via Lyapunov theory. Hardware-in-the-loop simulations using MATLAB/Simulink and a PLC demonstrate that, under ±3 kPa pressure constraints, the maximum engine inlet pressure error is 1.782 kPa (77.1% lower than PID control), and under an 80 kg/s² flow-rate disturbance, valve oscillations decrease from ±27% to ±5%. These results confirm the superior disturbance rejection and decoupling performance of the proposed method. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article

To enhance the speed control performance of the permanent magnet synchronous motor (PMSM) servo system, an improved sliding mode control method integrating a torque observer is presented. The current loop uses current feedback decoupling PID control, and the speed loop applies sliding mode control. In comparison to previous work in hybrid SMC using fuzzy logic and torque observers, this p proposes a hyperbolic tangent function in replacement of the signum function to solve the conflict between rapidity and chattering in the traditional exponential reaching law, and fuzzy and segmental self-tuning rules adjust relevant switching terms to reduce chattering and improve the sliding mode arrival process. A load torque observer is designed to enhance the system’s anti-interference ability by compensating the observed load torque to the current loop input. Simulation results show that compared with traditional sliding mode control with a load torque observer (SMC + LO), PID control with a load torque observer (PID + LO), and Active Disturbance Rejection Control (ADRC), the proposed strategy can track the desired speed in 0.032 s, has a dynamic deceleration of 2.7 r/min during sudden load increases, and has a recovery time of 0.011 s, while the others have relatively inferior performance. Finally, the model experiment is carried out, and the results of the experiment are basically consistent with the simulation results. Simulation and experimental results confirm the superiority of the proposed control strategy in improving the system’s comprehensive performance. Full article