Search Results (24)

Conventional active disturbance rejection control (ADRC) schemes for grid-connected inverters (GCIs) suffer from performance degradation under grid frequency fluctuations and voltage distortions, primarily due to the limited bandwidth of conventional extended state observers (ESOs) and their reliance on fixed-frequency internal models. To address these issues, this paper proposes a second-order repetitive ADRC (SRC-ADRC) strategy for LCL-type single-phase GCIs. The proposed method integrates a second-order repetitive extended state observer (SRC-ESO) and a frequency-adaptive proportional-quasi resonant (FPQ) controller. By cascading an additional internal model into the repetitive control-based ESO, the SRC-ESO achieves higher internal model gain and wider resonant bandwidth, significantly improving periodic disturbance estimation accuracy under off-nominal grid frequencies. The FPQ controller ensures reference current tracking without phase error and amplitude attenuation, while the control law and observer are decoupled for independent parameter design. The robustness and frequency adaptability of the SRC-ADRC are verified through theoretical stability analysis and frequency-domain evaluation. Experiments on a 1.5 kW LCL-type single-phase GCI platform show that SRC-ADRC provides better frequency robustness, stronger harmonic mitigation, and improved current-tracking performance than the comparison methods. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a $+27.36\%$ reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to $15.2 \mathrm{s}$, and restricting transient overshoot to just $0.18\%$. Full article

Permanent magnet synchronous generators (PMSGs) are inevitably subject to aperiodic and periodic disturbances due to complex operating conditions and internal coupling effects. To improve speed regulation under such disturbances, this paper develops a hierarchical control framework that integrates a parameter-decoupled extended state observer (DESO) with quasi-resonant control. A novel parameter decoupling method enables independent tuning of the observer bandwidth and controller parameters, while the quasi-resonant control module specifically targets periodic torque ripples caused by the tower shadow effect. Simulation results under stochastic wind conditions confirm that the proposed QR-DESO significantly outperforms conventional methods, reducing the speed tracking root mean square error (RMSE) by $61.8\%$ and the total harmonic distortion (THD) to $0.17\%$. The system also exhibits strong robustness against $\pm 20\%$ parameter mismatches, validating its effectiveness for offshore wind power applications. Full article

To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model of the quadrotor is established. Secondly, a parameterized tanh function is introduced and applied to design the tracking differentiator, extended state observer, and nonlinear feedback control law. Then, the TD3 learning mechanism is incorporated to automatically learn and optimize controller parameters, thereby significantly enhancing the system’s disturbance rejection capability. Finally, simulation studies comparing conventional PID, ADRC, DDPG and the proposed TD3-ADRC algorithms are conducted in Simulink. In addition, a bench test system is developed using the PX4 flight controller. Experimental results show that, under complex environmental conditions, the proposed TD3-ADRC controller outperforms both conventional PID and linear ADRC methods in terms of reliability and adaptability, validating the effectiveness of the proposed control approach. Full article

To mitigate rotor vibrations in magnetic bearing systems arising from mass imbalance, this study proposes a novel suppression strategy that integrates the crested porcupine optimizer (CPO) with an enhanced linear active disturbance rejection control (ELADRC) framework. The approach introduces a disturbance estimation and compensation scheme based on a linear extended state observer (LESO), wherein both the LESO bandwidth ${\omega }_0$ and the LADRC controller parameter ${\omega }_c$ are adaptively tuned using the CPO algorithm to enable decoupled control and real-time disturbance rejection in complex multi-degree-of-freedom (DOF) systems. Drawing inspiration from the crested porcupine’s layered defensive behavior, the CPO algorithm constructs a state-space model incorporating rotor displacement, rotational speed, and control current, while leveraging a reward function that balances vibration suppression performance against control energy consumption. The optimized parameters guide a real-time LESO-based compensation model, achieving accurate disturbance cancelation via amplitude-phase coordination between the generated electromagnetic force and the total disturbance. Concurrently, the LADRC feedback structure adjusts the system’s stiffness and damping matrices to improve closed-loop robustness under time-varying operating conditions. Simulation studies over a wide speed range (0~45,000 rpm) reveal that the proposed CPO-ELADRC scheme significantly outperforms conventional control methods: it shortens regulation time by 66.7% and reduces peak displacement by 86.8% under step disturbances, while achieving a 79.8% improvement in adjustment speed and an 86.4% reduction in peak control current under sinusoidal excitation. Overall, the strategy offers enhanced vibration attenuation, prevents current saturation, and improves dynamic stability across diverse operating scenarios. Full article

The electrification of agricultural machinery has become an important trend. Active Disturbance Rejection Control (ADRC) shows considerable potential in agricultural motor control due to its low model dependence and strong anti-disturbance capability. However, the Extended State Observer (ESO) of traditional ADRC is limited by bandwidth, making it difficult to effectively capture high-frequency impact disturbances such as torque fluctuations during straw cutting, which results in reduced efficiency and increased energy consumption. To address this, this paper proposes an improved ADRC scheme: designing a Super-Twisting Extended State Observer (STESO) by integrating Super-Twisting technology to enhance disturbance observation capability; incorporating a Quasi-Proportional Resonant (QPR) controller into the Error Feedback Control Law (SEF) to compensate for the shortcoming of disturbance suppression beyond ESO bandwidth; and proposing a decoupling strategy to reduce the difficulty of parameter tuning and optimize control performance. Simulations and experiments on the Permanent Magnet Synchronous Motor (PMSM) of an automatic seeder demonstrate that the proposed method can effectively suppress various disturbances, reduce speed regulation errors, and not deteriorate dynamic responses. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications. Full article

This study proposes an event-triggered fault-tolerant active disturbance rejection control (ADRC) method for variable-load quadrotors with prescribed performance. The quadrotor, as a nonlinear and underactuated system, faces challenges such as payload variations, actuator faults, and external disturbances, which degrade trajectory tracking accuracy and stability. The proposed approach integrates a cascaded ADRC framework, decoupling the system into position and velocity subsystems, each equipped with extended state observers (ESOs) for real-time disturbance estimation and compensation. To enhance robustness, prescribed performance functions dynamically constrain tracking errors within predefined bounds, while event-triggered mechanisms reduce computational load through condition-based updates of control signals. Additionally, a particle swarm optimization (PSO) algorithm is employed for online parameter tuning, improving adaptability. Theoretical analysis confirms the system stability, and simulation results demonstrate the controller effectiveness in handling actuator faults and variable payloads, ensuring accurate trajectory tracking and reduced resource consumption. The method offers a promising solution for robust and efficient quadrotor control in complex environments. Full article

This paper proposes an integrated control framework for improving the trajectory tracking performance of autonomous ground electric vehicles (AGEVs) under complex disturbances, including parameter uncertainties, and environmental changes. The framework integrates active disturbance rejection control (ADRC) for real-time disturbance estimation and compensation with a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm for dynamic optimization of controller parameters to improve tracking accuracy and robustness. More specifically, it combines the Line of Sight (LOS) guidance rate with ADRC, proves the stability of LOS through the Lyapunov law, and designs a yaw angle controller, using the extended state observer to reduce the impact of disturbances on tracking accuracy. And the approach also addresses the nonlinear vehicle dynamic characteristics of AGEVs while mitigating internal and external disturbances by leveraging the inherent decoupling capability of ADRC and the data-driven parameter adaptation capability of DDPG. Simulations via CarSim/Simulink are carried out to validate the controller performance in serpentine and double-lane-change maneuvers. The simulation results show that the proposed framework outperforms traditional control strategies with significant improvements in lateral tracking accuracy, yaw stability, and sideslip angle suppression. Full article

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article

Achieving excellent speed control in permanent magnet synchronous motors (PMSMs) relies on the simultaneous suppression of both aperiodic and periodic disturbances. This paper presents an enhanced Active Disturbance Rejection Control (ADRC) strategy specifically designed to address these disturbances in single-rotor compressors (SRCs). To achieve simultaneous suppression, a Recursive Gauss–Newton (RGN) algorithm is implemented in parallel with the conventional extended state observer (ESO) to enhance the ADRC framework. The RGN algorithm iteratively estimates the amplitude and phase information of periodic disturbances, while the ESO primarily observes the system’s aperiodic disturbances. In contrast to existing methods, the proposed angle-based approach demonstrates superior performance during speed transients. Detailed convergence and decoupling analyses are provided to facilitate parameter tuning. The effectiveness of the proposed method is validated through simulations and experiments conducted on a 650 W SRC, demonstrating its superiority over proportional–integral (PI) control, conventional ADRC, and quasi-resonant controller-based ADRC (QRC-ADRC) under both steady-state and dynamic conditions. Full article

The continuously increasing requirements for product purity and heat exchange efficiency in distillation processes exacerbate the system’s nonlinearity, coupling effects, and uncertainties. To address these challenges, this research proposes an optimized design approach for multivariable active disturbance rejection control (ADRC) that integrates quantitative feedback theory (QFT). An extended state observer is first employed to estimate and compensate for coupling and uncertainties, thus enabling effective decoupling. Under a two-degree-of-freedom equivalent model, QFT performance boundaries are transformed into a fitness function, turning controller parameter tuning into a frequency-domain multi-objective optimization problem. An improved multi-objective grey wolf algorithm is then introduced to optimize the controller parameters. The proposed approach is verified in a toluene–methylcyclohexane (MCH) extractive distillation process and compared with proportional–integral (PI) control and model predictive control (MPC). The simulation results indicate that, under the same feed temperature disturbance, the ADRC–QFT strategy reduces the system settling time by over 67% and lowers the integral of absolute error (IAE) index by more than 53% compared with PI–QFT and MPC, while also exhibiting stronger robustness to model uncertainties. These findings suggest that the proposed method provides an effective solution for achieving high precision and robust control in complex coupled distillation processes. Full article