Contraction-based active disturbance rejection controller for an active ankle foot orthosis

Intelligent control framework for Unmanned Aerial Vehicle autonomous docking based on Linear Active Disturbance Rejection Control and improved Particle Swarm Optimization

The advancement of modern maritime operations increasingly depends on reliable and intelligent control technologies. Dynamic Positioning (DP) serves as a crucial guarantee for the stable operation of marine vessels. Nevertheless, owing to the inaccuracies and coupling effects in ship dynamic models, designing a controller that can adapt to the complex marine environment remains a formidable challenge. Given the chattering and phase lag issues in the traditional Active Disturbance Rejection Controller (ADRC), this paper proposes a nonlinear filtering module and a phase compensator to improve ADRC performance. Additionally, to minimise the impact of noise on the Tracking Differentiator (TD), the traditional fast optimal control module is replaced with a hyperbolic tangent function. Moreover, to optimise the multiple internal parameters of the Improved ADRC (IADRC), this paper adopts the Salp Swarm Algorithm (SSA). Due to the tendency to fall into local optima, the low convergence accuracy and the slow convergence speed of SSA, this paper proposes Tent mapping, a disturbance factor and an adaptive rate to ameliorate the parameters optimisation. The relative simulation results show that the DP system with Improved SSA (ISSA)-based IADRC has the advantages of small tracking error and strong disturbance rejection capability.

Dual linear motor-driven systems (DLMDS) are widely used in industrial manufacturing due to their high dynamic stability and robust performance, typically featuring a symmetric Y1–Y2 axis structure. High-precision synchronization control of the motion platform is crucial for overall system performance. However, in practice, such systems are inevitably affected by mechanical installation errors, load disturbances, and nonlinear friction, which lead to the asymmetry of the Y1–Y2, severely degrading the synchronization accuracy between the two symmetric axes. To address these challenges, this paper proposes an EtherCAT-enabled active disturbance rejection control (ADRC) strategy for high-performance gantry synchronization systems. To cope with strong coupling effects, external disturbances, and high-speed operation, a master–slave synchronization architecture is developed based on ADRC and the EtherCAT cyclic synchronous torque (CST) mode. An extended state observer (ESO) is employed to estimate and compensate for lumped disturbances in real time, enabling precise synchronization without relying on an accurate mechanical model. Experimental results under both low-speed and high-speed operating conditions show that the proposed method significantly improves the synchronization stability and robustness compared with conventional cross-coupling control and master–slave control strategies. Specifically, the ADRC-based approach reduces synchronization errors by more than 20% under disturbance-free conditions and suppresses approximately 80% of disturbance-induced errors during high-speed operation. These results confirm the effectiveness and practical applicability of the proposed control strategy for high-precision gantry motion systems. Unlike conventional torque-mode implementations that merely replace the position loop with torque regulation, the proposed method introduces a disturbance-estimation-driven synchronization architecture co-designed with deterministic EtherCAT cyclic timing, which enables distributed real-time compensation beyond classical torque feedforward strategies.

This study proposes a systematic approach for implementing discrete-time Linear Active Disturbance Rejection Control in the closed-loop regulation of power converters. The continuous-time Linear Extended State Observer was discretized using the zero-order hold method to obtain a current estimator-based Linear Extended State Observer that is suitable for real-time implementation. The design considerations for discrete-time Linear Active Disturbance Rejection Control, including the selection of observer and controller parameters and the sampling period, are addressed. For performance comparison, a PI controller was designed and implemented in discrete time. The control schemes were evaluated via MATLAB/Simulink (2025b) simulations and real-time closed-loop experiments on a microcontroller to assess the transient response, disturbance rejection capability, and steady-state accuracy of the buck converter. The simulation and experimental results demonstrate that the discrete-time Linear Active Disturbance Rejection Control incorporating a current-estimator-based Linear Extended State Observer significantly outperforms the PI controller in terms of transient response and disturbance rejection capability. From this perspective, this study provides a meaningful contribution to the limited literature on linear extended state observer-based discrete-time Active Disturbance Rejection Control methods.

Flexible-joint manipulators suffer from severe performance degradation due to the coupling of joint elasticity and varying loads. To address this, we propose a composite finite-time active disturbance rejection control (CFT-ADRC) strategy utilizing a frequency-domain separation architecture. A recursive least squares (RLS) algorithm identifies slow-varying load parameters, while an extended state observer (ESO) compensates for high-frequency unmodeled dynamics and external disturbances, effectively preventing loop interference. A finite-time control law guarantees rapid tracking error convergence. Comprehensive simulations confirm that this approach significantly outperforms standard ADRC and neural network-based methods (RBFNN-ASMC). Under 50% load variations, it achieves an RMS tracking error of 2×10−3 rad and maintains robust stability during 200% instantaneous load mutations. The strategy presents a strong theoretical framework for future hardware implementation while maintaining an optimal balance of precision, robustness, and computational simplicity.

This study investigates the impact of time-varying jet reaction forces on ship-cleaning manipulators, focusing on joint torque fluctuations, high-frequency chatter, and the deterioration of end-effector positioning accuracy. An enhanced active disturbance rejection control (ADRC) method is proposed, which integrates jet reaction force modeling, joint torque feedforward compensation, and improvements to both the extended state observer (ESO) and the nonlinear state error feedback (NLSEF). Simulation results show that the enhanced ADRC reduces the joint stabilization time from 10 to 2 seconds, suppresses over 70% of the initial torque spikes, and decreases the ESO observation errors by more than 60%. Compared with the classical sliding mode control (SMC), the proposed method effectively eliminates high-frequency chattering phenomena and reduces control signal aggressiveness during the startup phase. This method improves trajectory tracking accuracy under strong disturbances while smoothing the control output and significantly reducing high-frequency joint chatter. These results confirm that the enhanced ADRC method improves stability and responsiveness, providing a robust framework for the reliable operation of ship-cleaning manipulators in high-disturbance environments.

High-precision cooperative control of dual-arm manipulators faces significant challenges arising from complex dynamic coupling, parametric uncertainties, and external disturbances. Furthermore, in networked control scenarios, communication bandwidth and computational resources are inevitably constrained. To address these issues, this paper proposes a novel composite control framework that integrates adaptive dynamic programming (ADP) with active disturbance rejection control (ADRC) under a static event-triggering mechanism (SETM). First, to handle model uncertainties and external perturbations, a smooth nonlinear extended state observer (ESO) based on continuous fractional-power functions is developed. This observer guarantees finite-time convergence of the disturbance estimation without inducing the high-frequency chattering inherent in conventional sliding-mode observers. Second, leveraging the disturbance-compensated dynamics, a radial basis function (RBF) neural network-based ADP controller is designed to learn the optimal control policy online, thereby minimizing a quadratic performance index without requiring accurate model knowledge. Third, to improve resource utilization, a static event-triggering strategy is introduced to schedule control updates based on the system state and tracking error. Extensive simulation studies on a 3-DoF dual-arm system demonstrate that the proposed scheme achieves superior trajectory tracking accuracy and disturbance robustness while significantly reducing the communication frequency compared to time-triggered approaches.

To address the issue that current ripple in traditional switching power amplifiers (SPA) for active magnetic bearing (AMB) systems is constrained by the switching frequency, this paper proposes a novel LCL filter-based switching power amplifier (LCL-SPA) along with its parameter design and high-performance control strategy. Without altering the original full-bridge topology or the switching frequency, the proposed scheme achieves superior ripple suppression. To tackle the inherent resonance problem of the LCL filter, a sensorless capacitor current feedback active damping (CCFAD) strategy is proposed. This approach effectively suppresses resonance without additional hardware sensors and ensures system stability under digital control delays. Furthermore, to overcome the limitations of traditional PI controllers in terms of the dynamic performance and parameter tuning of the LCL-SPA, a high-performance LESO-based control algorithm within the Linear Active Disturbance Rejection Control (LADRC) framework is designed. By utilizing a Linear Extended State Observer (LESO) to estimate and compensate for total lumped disturbances in real-time, the algorithm simplifies the parameter tuning process and achieves rapid current tracking with nearly zero overshoot. Experimental results demonstrate that the proposed LCL-SPA achieves extremely low current ripple across various reference currents, with the ripple minimized to 20 mA at a 3 A load. Frequency response tests confirm that the system possesses a closed-loop bandwidth of up to 2 kHz, satisfying the high dynamic requirements of magnetic bearings.

Active disturbance rejection control synthesis for industrial time-delayed process: an observation reconfiguration perspective.

Lane following with obstacle avoidance for unmanned tracked vehicles using monocular vision and active disturbance rejection control

An improved active disturbance rejection control for voltage stabilization and performance enhancement in hybrid photovoltaic battery based DC microgrids via red-tailed hawk algorithm

Characteristic analysis and model predictive-improved active disturbance rejection control of direct-drive electro-hydrostatic actuators

Modified active disturbance rejection control for low-altitude logistics transport fixed-wing unmanned aerial vehicles

Design of a temperature control system for immersion liquid utilizing cascade active disturbance rejection control and PI algorithm

This paper introduces an enhanced load frequency regulation strategy for isolated renewable microgrids, leveraging an Active Disturbance Rejection Control (ADRC) framework optimized through Elephant Herding Optimization (EHO). A detailed microgrid model, encompassing a variety of energy generation and storage units, is implemented in a simulation environment. The effectiveness of the proposed ADRC-EHO method was assessed through comparative analysis with established control techniques: Particle Swarm Optimization (PSO)-tuned ADRC and H∞ control under diverse operational scenarios. These scenarios included deterministic and stochastic load disturbances, as well as variations in microgrid parameters. The findings demonstrate that the ADRC-EHO approach consistently yields superior performance, with improved robustness and a more rapid response to frequency fluctuations. The optimization of ADRC parameters using EHO effectively countered the challenges of intermittent renewable energy integration.

Parameter tuning for Active Disturbance Rejection Control (ADRC) in rolling mill hydraulic synchronization systems is critical for enhancing strip quality. Conventional manual trial-and-error methods often yield suboptimal results. This paper proposes a hybrid algorithm, WMA-PSO, integrating the Humpback Whale Migration Algorithm (WMA) with Particle Swarm Optimization (PSO) through an adaptive fusion weight strategy. This approach effectively balances global exploration and local exploitation, improving optimization accuracy and efficiency. Evaluation on the CEC-2005 benchmark suite shows that WMA-PSO outperforms several state-of-the-art algorithms. Simulation experiments on ADRC tuning in a rolling mill system demonstrate that the WMA-PSO-optimized controller achieves the smallest synchronization error and superior overall control performance compared to other methods. The results validate WMA-PSO as an effective tool for automated parameter tuning in complex industrial control systems.

Through this paper, an active disturbance rejection control scheme is designed based on an extended state observer capable of estimating the system’s internal variables and external disturbances without the need for expensive sensors and also attenuates sensor-induced noise, supporting cleaner measurements. The extended state observer is dynamically adjusted using fuzzy logic techniques. The proposed method is validated in Matlab/Simulink, with the results showing a significant reduction in both body displacement and acceleration compared to passive suspension systems, representing a direct improvement in vehicle stability and ride comfort; this demonstrates the robustness and adaptability of the proposed system. The evaluation covers three road excitations, sinusoidal, step, and trapezoidal, to broaden the analysis under both smooth and abrupt disturbances.

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches.

A two-layer control framework for unmanned underwater vehicle (UUV) navigation is proposed, combining a lower-layer active disturbance rejection controller (ADRC) with an upper-layer safe reinforcement learning (RL) policy for obstacle-avoidance navigation. The lower layer, utilizing ADRC, ensures high tracking accuracy and effective disturbance rejection, while the upper layer integrates the twin delayed deep deterministic policy gradient (TD3) algorithm, combined with a control barrier function (CBF)-based quadratic programming (QP) safety filter and safety-inspired reward shaping (SR). The method is evaluated in two simulation studies: (i) velocity and attitude control to assess tracking and disturbance rejection, and (ii) obstacle-avoidance navigation to assess learning efficiency, trajectory smoothness, and safety-related metrics. Simulation results show that ADRC achieves faster tracking and stronger disturbance rejection than a conventional proportional–integral–derivative (PID) controller. Moreover, the proposed TD3 + QP + SR scheme exhibits faster learning, smoother trajectories, and improved safety performance compared with RL baselines. These results indicate that the proposed framework enables efficient and safe UUV navigation in simulation scenarios with obstacles and disturbances.