Search Results (38)

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

Frequency regulation (FR) constitutes a fundamental aspect of power system stability, particularly in the context of the growing integration of intermittent renewable energy sources (RES) and electric vehicles (EVs). The load frequency control (LFC) mechanism, essential for achieving FR, is increasingly reliant on communication infrastructures that are inherently vulnerable to cyber threats. Cyberattacks targeting these communication links can severely compromise coordination among smart grid components, resulting in erroneous control actions that jeopardize the security and stability of the power system. In light of these concerns, this study proposes a cyber-physical LFC framework incorporating a fuzzy linear active disturbance rejection controller (F-LADRC), wherein the controller parameters are systematically optimized using the quasi-opposition-based reptile search algorithm (QORSA). Furthermore, the proposed approach integrates a comprehensive cyberattack detection and prevention scheme, employing Haar wavelet transforms for anomaly detection and long short-term memory (LSTM) networks for predictive mitigation. The effectiveness of the proposed methodology is validated through simulations conducted on a restructured power system integrating RES and EVs, as well as a modified IEEE 39-bus test system. The simulation outcomes substantiate the capability of the proposed framework to deliver robust and resilient frequency regulation, maintaining system frequency and tie-line power fluctuations within nominal operational thresholds, even under adverse cyberattack scenarios. Full article

Active disturbance rejection control is implemented in a LC-filtered surface-mounted permanent magnet synchronous motor (SPMSM) drive system to enhance current control dynamics. However, the combined effects of computation one-beat delay and the pulse-width modulation zero-order hold (ZOH) effect significantly degrade system stability and dynamic performance. To address these limitations, an improved predictive extended state observer (ESO) with an accurate ZOH discretization method is proposed to ensure fast and robust dynamic performance. The predictive ESO predicts one beat to compensate for the delay effect, while the ZOH discretization yields a more precise discrete dynamic model of the system. These combined improvements substantially enhance the system’s phase and gain margins, leading to superior dynamic performance. Furthermore, a discrete-domain transfer function of the control system is analytically derived, with the control parameters systematically designed using frequency-domain analysis to guarantee robust performance. Experimental validation on a LC-filtered SPMSM drive system demonstrates remarkable enhancement in current control dynamics while maintaining sufficient robustness. 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

In modern marine vessels equipped with electric propulsion systems, rectifiers are commonly used as part of the setup. However, the conventional deadbeat predictive direct power control strategy for three-phase voltage source pulse-width modulation (PWM) rectifiers tends to underperform when subjected to load variations and external disturbances. To address these limitations, this paper proposes an enhanced linear active disturbance rejection control (LADRC), incorporating virtual capacitance and an improved equivalent input disturbance strategy. The integration of virtual capacitance in the LADRC is specifically applied during load transitions. Virtual capacitance is a capacitor element simulated through the control strategy. It enhances voltage stability and dynamic response capability by compensating for voltage fluctuations and power deficits in the system. By providing a virtual active power, this approach substantially improves power tracking performance, reducing the DC voltage drop and settling time by 60% and 74%, respectively. In addition, the proposed strategy is easy to implement and does not add complexity to the LADRC. Moreover, the equivalent input disturbance is refined through virtual capacitance, enabling accurate disturbance estimation. As a result, the active power ripple and current total harmonic distortion under disturbances are reduced by 44% and 40%, respectively. The stability of the proposed strategy is comprehensively analyzed, and experimental results from a prototype system validate its effectiveness and accuracy. Full article

One of the most important contributions of modern control theory from the 1960s was the separation of the dynamics of state-space controller design from the dynamics of state reconstruction. However, because modern control theory predates the mass spread of digital controllers and was predominantly focused on analog solutions that avoided modeling dead-time elements, it cannot effectively cover all aspects that emerged with the development of programmable devices and embedded systems. The same historical limitations also characterized the development of proportional-integral-derivative (PID) controllers, which began several decades earlier. Although they were used to control time-delayed systems, these solutions, which are most commonly used in practice today, can also be referred to as simplified disturbance observers that allow the avoidance of the the direct use of dead-time models. Using the example of controlling systems with a double integrator plus dead-time model, this article shows a novel controller design that significantly improves control performance compared to conventional PID controllers. The new control structure is a combination of a generalized state-space controller, interpreted as a higher-order derivative controller, and a predictive disturbance observer that uses the inversion of double integrator dynamics and dead-time models. It enables the elimination of the windup effect that is typical for PID control and extends the separation of the dynamics of setpoint tracking from the dynamics of state and disturbance reconstruction to time-delayed processes as well. The novelty of the presented solution offers several orders of magnitude lower amplification of measurement noise compared to traditional PID control. On the other hand, it offers high robustness and a stable transient response despite the unstable internal feedback of processes like the magnetic levitation system. The improvements achieved are so high that they call into question the classical solutions with PID controllers, at least for DIPDT models. In addition to the comparison with PID control, the relationship with traditional state space controllers, which today form the basis of active disturbance rejection control (ADRC), is also discussed and examined for processes including dead time. Full article

Ensuring that the world’s population meets its food needs, despite water restrictions, can be significantly improved by increasing irrigation efficiency and productivity. Achieving this goal necessitates technological advancements in control systems. Therefore, the implementation of effective control systems across the entire irrigation water distribution chain is crucial and requires technological modernization. This paper presents a control scheme that combines the benefits of model predictive control (MPC) and active disturbance rejection by using generalized proportional integral (GPI) observers. The proposed control scheme was applied to a three-canal irrigation system. The simulation results confirm that the proposed controller is robust to disturbances and ensures accurate tracking for all reference levels. The controller’s performance is highlighted by the improvement in response time and considerable reduction in overshoot compared with the optimized proportional integral (PI) controllers. Additionally, the use of GPI observers allows for the precise estimation of nonlinear disturbances and phase variables, enhancing the robustness of the system. The efficiency of the observer is due to its ability to adequately estimate global additive disturbances, including unknown parameters and external disturbances in the input–output dynamics. Full article

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions. Full article

Aiming at the DC bus voltage instability problem resulting from the stochastic nature of distributed energy output and load fluctuation, an Integral Sliding Mode Linear Active Disturbance Rejection Control (ISMLADRC) combined with Model Predictive Control (MPC) strategy for energy storage bi-directional DC–DC converter is proposed based on the wind–solar-storage DC microgrid system. The proposed method utilizes a dual closed-loop control structure, employing a two-step MPC approach in the inner current loop to enhance the system’s dynamic response performance. Secondly, the integral sliding mode control algorithm is embedded into the Linear Active Disturbance Rejection (LADRC) technique, based on which the outer voltage loop ISMLADRC controller is designed to mitigate the impact of disturbances on the bus voltage effectively, and the system’s anti-disturbance performance and robustness are improved. Considering that the parameters of the ISMLADRC controller are too many to be tuned, and in order to give full play to the performance of the controller, an Improved Dung Beetle Optimization Algorithm (IDBO) is used to tune the parameters. Finally, based on MATLAB 2022b and RT-LAB 2021.3 platforms, the simulation results show that in a variety of operating conditions, the proposed control strategy can flexibly regulate the bidirectional transmission of battery energy, and the bus voltage recovery time is within 0.1 s, and the voltage fluctuation is less than 0.5%. Full article

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions. Full article

In DC microgrids, a large-capacity hybrid energy storage system (HESS) is introduced to eliminate variable fluctuations of distributed source powers and load powers. Aiming at improving disturbance immunity and decreasing adjustment time, this paper proposes active disturbance rejection control (ADRC) combined with improved MPC for n + 1 parallel converters of large-capacity hybrid energy storage systems. ADRC is utilized in outer voltage control loops, and improved MPC is employed in inner current control loops of n battery converters. Droop control is adopted to obtain power distribution between n battery converters, and a DC bus voltage compensator is used to compensate voltage deviations and maintain constant DC bus voltage. The low-pass filter (LPF) is adopted to obtain high-frequency power as the reference for the supercapacitor converter, ADRC is also utilized in the outer power control loop, and MPC is employed in the inner current control loop. Compared with traditional observers, the voltage expansion state observer of the proposed ADRC control is independent of the system model and parameters and consequently has strong disturbance immunity, and significantly reduces voltage overshoots during power fluctuations. The MPC-based inner current control loops of n + 1 converters accelerate current response speed and significantly decrease switching losses. Simulation and experimental results indicate that utilizing the proposed control strategies, large-capacity HESS has stronger anti-interference ability, shorter regulation time, smaller switching loss, and simultaneously maintains the stability of the DC bus voltage. Full article

Lately, active disturbance rejection control (ADRC), a model-independent controller, has become popular for combating various forms of uncertain disturbances incurred in industrial applications. ADRC was validated for external disturbances, internal disturbances, and nonlinearities incurred under realistic scenarios. Time delay challenges all controllers, especially when it coexists with nonlinearities. This paper investigates the impacts of time delay and backlash-like hysteresis nonlinearity in ADRC-controlled systems. These impacts are analyzed, as in the case study, in two ADRC-based methods, namely the ADRC with delayed input method and the predictive extended state observer (PESO)-based ADRC (PESO-ADRC) method. To improve the system response and to attain a decent attenuation of uncertainties, the time-delay estimation (TDE) mechanism is introduced to the concerned ADRC-based methods. Experimental studies are conducted to verify the effectiveness and stability of the proposed TDE-ADRC methods. The results demonstrate the robustness and decent recovery of the transient response after the adverse impact of the backlash-like hysteresis on both concerned ADRC-controlled systems. Full article

This article discusses the problem of controlling the trajectory of a flapping-wing aircraft in the face of external disturbances. As the applications for flapping-wing aircraft have diversified, the external disturbances to which the system is exposed have become more complex. Existing control methods have difficulty with effectively counteracting these disturbances. Therefore, this paper suggests a control method that combines linear active disturbance rejection with model predictive control to solve the tracking problem under disturbances, improve the system’s disturbance rejection capability, and ensure the accuracy of trajectory tracking. First, a linear active disturbance controller (LADRC) is developed for the position system to monitor and compensate for internal uncertainties and environmental disturbances in a timely manner. Secondly, the attitude control system is equipped with a model predictive controller (MPC) to effectively determine the optimal control variables and achieve stable attitude tracking. The method is evaluated through simulation studies to assess its performance in tracking a reference trajectory in the presence of disturbances. The findings demonstrate that the approach can accurately track the reference trajectory even when the system is subject to sinusoidal disturbances. This indicates that the method exhibits robustness and practicality. Full article

This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles ${\theta }_1$and ${\theta }_2$, and contact force $f$, are $1.6636\times 10^{-28} \mathrm{r}\mathrm{a}{\mathrm{d}}^2\cdot \mathrm{s}$, $1.7983\times 10^{-28} \mathrm{r}\mathrm{a}{\mathrm{d}}^2\cdot \mathrm{s}$, and $1.8062\times 10^{-30} {\mathrm{N}}^2\cdot \mathrm{s}$, respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation. Full article

This work addresses the motion control problem for a 4-degree-of-freedom unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. A disturbance observer-based control scheme is proposed, which is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the developed EAOB has the ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms an effective adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and robot operating system environment. The results show that the proposed method can effectively reject unpredictable disturbances and improve the UUV’s control performance. Full article