Disturbance Rejection Research Articles

Active disturbance rejection control for four-wire inverters in standalone renewable resources-based microgrid -islanded microgrids ADRC-based control-

Active disturbance rejection control for four-wire inverters in standalone renewable resources-based microgrid -islanded microgrids ADRC-based control-

Adaptive iterative learning control for enhancing the dynamic path tracking accuracy of 6-degrees of freedom industrial robots

In this article, an adaptive iterative learning control (AILC) scheme has been proposed to enhance the accuracy of the dynamic path tracking of 6-degrees of freedom industrial robots. Based on the memorized data and current feedback from a three-dimensional visual measurement instrument, an adaptive algorithm is developed to update the time-varying control parameters of the AILC scheme iteratively. A new compensation signal is calculated to adjust the control inputs produced by the dynamic path tracking control module at each time interval. Through the adaptation algorithm, the identical initial conditions can be relaxed to some extent with the AILC scheme. Moreover, the stability analysis of the proposed AILC scheme is presented. Experimental results on FANUC M20iA, using C-Track 780 as a photogrammetry sensor, demonstrate the superior performance of the developed AILC scheme in terms of pose accuracy, disturbance rejection ability, and control performance.

Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems

In this study, a comparative analysis of three different control methods for precise, real-time control of a complex dynamic double-tank liquid level process system was performed. Since the system in question has a time-delayed structure, feedforward proportional integral (FF-PI) control and cascaded nonlinear feedforward proportional integral delayed (CNPIR) controllers were tested on the process system. While the FF-PI controller improved the response time of the system, it showed limitations in handling external disturbances and nonlinearities. On the other hand, the CNPIR controller showed better improvements in control accuracy and lower overshoot compared to the FF-PI controller. Since the process system has a nonlinear model and is affected by external disturbances, these two controllers were inadequate in this study when compared to the fractional order adaptive proportional integral derivative sliding mode controller (FO-APIDSMC). The FO-APIDSMC controller provided fairly good performance in both tracking accuracy and disturbance rejection control for non-chattering, fast finite-time convergence, increased robustness, and uncertain dynamic processes. Experimental results reveal that the FO-APIDSMC controller achieves superior minimized tracking error and outperforms the FF-PI and CNPIR controllers by effectively handling uncertainties and external disturbances.

Active Disturbance Rejection Control for Flux Weakening in Interior Permanent Magnet Synchronous Motor Based on Full Speed Range

To address the impact of load disturbances on the full-speed-range control of an interior permanent magnet synchronous motor (PMSM), an active disturbance rejection control (ADRC) method is proposed. The speed loop employs phased field-weakening control (FW) based on ADRC, while the current loop utilizes proportional-integral-derivative (PID) control. Starting from the motor parameters, the Lagrange multiplier method was used to derive the critical speeds for the maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) ratios, and the timing for the field-weakening control was analyzed. A full-speed-range control model of the motor was established, and an ADRC-based speed loop controller was designed to achieve smooth transitions between high speeds and anti-disturbance solid capabilities. Based on the proposed control strategy, a 21 kW PMSM was used as the research object, and a full-speed-range control simulation model was developed in MATLAB/SIMULINK to verify the strategy. Compared to the traditional PID control, the simulation results demonstrate that the proposed strategy effectively observes and compensates for load disturbances, significantly reducing initial torque oscillations under three different operating conditions. After a sudden load increase, torque oscillations were reduced by 16%, with the stator current reaching steady state 0.03 s faster, response speed improving by 0.02 s, smooth transitions between speed ranges, and enhanced anti-disturbance performance.

A novel control strategy for PWM rectifier based on underactuated characteristics

In this paper, a double closed loop controller based on equivalent input disturbances (EID) error perturbation is proposed to solve the problem of abnormal operation of three-phase pulse width modulation (PWM) rectifier caused by load and external conditions. By analysing the buffeting problem and response time problem caused by traditional sliding mode in the current control process, a new current control method of terminal fuzzy sliding mode is designed. The system error is estimated by EID error estimation, and fuzzy control is introduced to improve the accuracy of error estimation, which solves the current buffeting and response problem. In order to solve the problem of response rate and harmonics in the voltage controller design, the extended state observer (ESO) in active disturbance rejection control (ADRC) control is improved, EID error estimation is introduced, the response rate and anti-disturbance ability of the system are further improved, and the harmonics of the system are suppressed. The experimental results show the effectiveness and superiority of the proposed method.

Voltage and Fuel Utilization Control Strategy for Solid Oxide Fuel Cell Based on Active Disturbance Rejection Control

ABSTRACTSolid oxide fuel cell (SOFC) systems have become a research focus because of their clean and high‐efficiency properties. Control of output voltage and fuel utilization is critical to the energy management for multi‐energy systems incorporating SOFC energy supply. However, maintaining precise control of the system's voltage in the presence of perturbations can be challenging. Moreover, the system's voltage control process can lead to fuel utilization fluctuations, which may affect the economy and safety. The design of the controller must meet both of these requirements. The stringent control requirements lead to poor parameter adaptability of existing controllers. This paper designs a nonlinear function and adopts a nonlinear/linear active disturbance rejection controller (ADRC) based on state switching to solve the output voltage tracking control problem of SOFC and maintain the fuel utilization rate in the ideal range. The simulation experimental results show that the proposed method has the advantages of strong and superior parameter adaptability with less control effort, which provides theoretical guidance for the design of the output voltage controller of the actual SOFC system.

Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

This paper concentrates on the design problem of linear active disturbance rejection control (LADRC) for uncertain systems, whose order and parameters are unknown. In general, LADRC has diversified structural forms due to different order and parameters selection. To clarify the prerequisites for constructing the LADRC strategy, a Laguerre-based LADRC (LLADRC) method is proposed, which provides controller design guidance for systems with unknown models by capturing the impulse response. Then, intensive attention is focused on establishing internal stability conditions of the feedback system and parameter tuning rules of the prescribed controller. To gain a better understanding, the internal correlation between LLADRC and standard LADRC of the same order is analyzed. It is shown that observer and controller gains of the standard LADRC can be tuned via a workable LLADRC to achieve better control performance. Finally, a numerical example and an application design of DC motor speed control are studied, both of which demonstrate the feasibility and validity of the proposed method.

Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot

ABSTRACT We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-tracking problems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve the tracking performance based on its trained model. The ANFIS parameters are tuned using both particle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results of two different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposed AFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFIS performance is evaluated and compared with those from other controllers using the average tracking error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach with a rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems

The number of distributed renewable energy sources of integrated energy systems (IESs) are increasing. The volatility of renewable energy exacerbates active power imbalances and increase frequency deviations in power systems. This work proposes a decomposition prediction fractional-order active disturbance rejection control deep Q network (DPFOADRCDQN) to control each generator of IESs more accurately and to reduce systemic frequency fluctuation. According to the fluctuation size of frequency deviation, the sub-signal obtained after the prediction of the modal decomposition of frequency deviation is classified into big fluctuating signals and small fluctuating signals; fractional-order active disturbance rejection control (FOADRC) controls small fluctuating signals; deep Q network (DQN) controls big fluctuating signals. Frequency deviation signals in complex power systems with a large percentage of renewable energy have higher smoothness after modal decomposition, FOADRC can efficiently follow the small fluctuating signals, and DQN can learn the rich data features in the big fluctuating signals and give precise control commands. The DPFOADRCDQN, proportional-integral-derivative, Q-learning, fuzzy control, and the previous state-of-the-art reinforcement learning are simulated in IESs where the renewable energy generators output is 0 %, 20 %, 40 %, 65 %, and 80 % of all generators output, the results show the average frequency deviation, total generation cost, and carbon emission cost are at least 42.7 %, 0.92 %, and 0.93 % smaller than the comparison algorithm, respectively.

An Improved Nonlinear Active Disturbance Rejection Controller via Sine Function and Whale Optimization Algorithm for Permanent Magnet Synchronous Motors Speed Control

Permanent magnet synchronous motors (PMSMs) speed control has gained wide application in various fields. Specifically, there is a disadvantage that nonlinear functions in the conventional active disturbance rejection controller (ADRC) is non‐differentiable at the piecewise points. Thus, an improved nonlinear active disturbance rejection controller (NLADRC) for permanent magnet synchronous motor speed control via sine function and whale optimization algorithm (WOA), abbreviated as NLADRC‐sin‐IWOA, is proposed to overcome this drawback. Considering the unsatisfactory control effect caused by the poor active disturbance resisting ability of the traditional PMSM controllers, this paper proposes an improved NLADRC for PMSM, that reconstructs a novel differentiable and smooth nonlinear function, the novel nonlinear function grounded on primitive function by the function of inverse hyperbolic, sine, square functions, and with difference fitting approach; and designs an improved whale optimization algorithm via convergence factor nonlinear decreasing, Gaussian variation and adaptive cross strategies. The experimental results findings show that the improved NLADRC‐sin‐IWOA has the advantages of response fast, small steady‐state error and tiny overshoot. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Simultaneous fault-tolerant consensus control and disturbance suppression for multi-agent systems with polynomial form fault under switching topology

For continuous-time linear leader-follower multi-agent systems with polynomial faults, the problem of fault-tolerant state consensus under switching topologies is studied. firstly, the average dwell time method is introduced, and observers are designed for different topologies to observe faults and disturbances. Secondly, according to the observed estimated value, the corresponding fault-tolerant control consensus protocol is proposed considering the switching situation of the communication topology. Finally, both theoretical analysis and simulation examples verify that the proposed design scheme can compensate faults and disturbance rejection.

A frequency domain fractional order tilted integral derivative controller design for fractional order time delay processes

AbstractIn real‐time processes, time delays are inherent due to various factors, such as delays in volume, mass, and information transfer, leading to a deterioration in process performance and stability. Therefore, this paper proposes a fractional‐order tilted integral derivative (TID) controller design for stable and unstable fractional‐order processes with time delay, where the optimal controller parameters are designed using a deterministic approach. An inner loop controller is designed using stability analysis and a graphical approach for unstable fractional‐order time delay processes. The proposed approach offers flexibility and fine‐tuning in designing the controller for a specific application. A systematic reference to the disturbance ratio is carried out to assess the disturbance handling capacity of the proposed controller. It shows the sensitivity of the designed controller against disturbances in the frequency spectrum graphically. Numerous performance indices and time‐domain parameters are computed and compared with state‐of‐the‐art techniques to analyze the efficacy. Furthermore, it is validated on the experimental setup of a four‐tank system. Simulation and experimental results demonstrate that the proposed approach outperforms recent techniques in terms of process control and disturbance rejection.

Robust load frequency control in interval power systems via reduced-order generalized active disturbance rejection control

Abrupt load changes, structural discrepancies, and parametric uncertainties cause degraded performance of the high-order power systems. This situation creates a problematic endeavor while analyzing the performance of such high-order systems. Hence, a simple and efficient lower-order control methodology can be deployed to sort out the issues related to load frequency control (LFC) in such systems. This study resolves the LFC problem in parametric bounded power systems by developing a worst-case reduced-order generalized active disturbance rejection control (WRGADRC) method. The core concept of the proposed technique entails that a controller will perform well in nominal scenarios if it performs satisfactorily in worst-case conditions. Therefore, an interval system’s worst-case reduced-order model is first obtained from its different uncertain models; the reduced order controller is then designed using the GADRC technique. The proposed scheme is rigorously validated on various parametric bounded minimum and non-minimum phase single-area and multi-area power systems, instilling confidence in its ability to achieve minimum frequency deviation in multiple scenarios. The supremacy of the proposed scheme is highlighted over some well-established control techniques in the literature related to the LFC problem.

Semi-active vibration control via a magnetorheological damper and active disturbance rejection control

This work provides an alternative for the semi-active vibration control problem via state feedback plus an active disturbance rejection control (K-ADRC) for a multistory building structure equipped with one magnetorheological damper (MRD) located between the ground and the first story. This control law introduces a disturbance observer (DOB) to estimate the total disturbance in real time, produced by the earthquake perturbation, unmodeled dynamics, and parametric uncertainties, and then cancel their effect using a part of the control signal. Moreover, using a diffeomorphism, the K-ADRC provides a proportional derivative (PD) controller designed to improve internal stability. A prominent feature of the active disturbance rejection control (ADRC) algorithm is that the value of the DOB gain is selected according to the system bandwidth. The stability of the system is verified via Lyapunov analysis. Moreover, the building structure model and the MRD are accomplished by incorporating a nonlinearity state that represents the hysteresis effect to perform real behavior in addition to assuming that acceleration is the only available measurement of building structures. Thus, integral filters are provided based on the appropriate cut-off frequency to estimate displacement and velocity, avoiding bias, offset, and phase shifts. Experimental results from a reduced-scale two-story building prototype demonstrate that the proposed K-ADRC is promising for real applications, and it has better performance than the state feedback (K) and the linear quadratic regulator (LQR) control techniques.

Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection

AbstractA mixed adaptive dynamic programming (ADP) scheme based on zero‐sum game theory is developed to address optimal control problems of autonomous underwater vehicle (AUV) systems subject to disturbances and safe constraints. By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. Initially, the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias. Also, the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset. To comprehensively leverage the advantages of model‐based and model‐free methods during training, an adaptive tuning factor is introduced based on the dataset that possesses model‐referenced information and conforms to the distribution of the real‐world environment, which balances the influence of model‐based control law and data‐driven policy gradient on the direction of policy improvement. As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. Moreover, the optimal control problem under disturbances is formulated as a zero‐sum game, and the actor‐critic‐disturbance framework is introduced to approximate the optimal control input, cost function, and disturbance policy, respectively. Furthermore, the convergence property of the proposed algorithm based on the value iteration method is analysed. Finally, an example of AUV path following based on the improved line‐of‐sight guidance is presented to demonstrate the effectiveness of the proposed method.

Dynamic performance enhancement of DFIG wind turbine using an active disturbance rejection control -based sliding mode control

The present paper proposes a hybrid control law that combines the active disturbance rejection control (ADRC) and the sliding mode control (SMC) to improve the decoupled control of the active and reactive power stator applied to a doubly-fed induction generator (DFIG) integrated into a wind energy conversion system. The stator is directly connected to the electric grid, while the rotor is connected to the electric grid via a back-to-back converter. The Lyapunov theory proves the stability of nonlinear control. The performance of the proposed strategy is compared with that of traditional ADRC and proportional-integral control (PI). The simulation results in MATLAB/SIMULINK environment using a 1.5MW wind system model show the proposed approach. The proposed algorithm shows an excellent dynamic performance that reduces the time response, overshoot, and steady-state error compared to the conventional controllers.Considering parametric variation, applying a novel controller results in fewer oscillations in stator powers, outperforming the PI regulator and classic ADRC.

Hybrid PLM and ADRC Control for Sensorless Induction Motor Drive with Nine-Level Converter Employing SVPWM

This paper outlines the design of a predictive controller combined with an active disturbance rejection control (ADRC)-type controller to enhance the dynamic performance of the induction motor powered by a nine-level converter. The predictive control law proposed is derived from the Poisson Laguerre model, based on predictive control. The motor is controlled using indirect field-oriented control, both with and without a speed sensor. For speed estimation, the Luenberger observer of order 4 is used. The predictive method utilized allows for the dynamic adjustment of control parameters based on those of the induction motor. The paper aims to eliminate internal and external disturbances and reduce total harmonic distortion (THD) through the use of a Nine-level cascaded H-bridge inverter. Space vector pulse width modulation (SVPWM) signals are generated using the logic of hexagon decomposition. The SVPWM method operates by decomposing higher-level hexagons into multiple two-level hexagons. The Poisson-Laguerre model (PLM) is also compared with the ADRC and the proportional-integral (PI) control. Simulations with MATLAB/SIMULINK software for an induction motor are performed to test the performance of each controller and the validity of the observer.

Active disturbance rejection control for systems with uncertainties and noise using super twisting differentiator-based event-triggered ESO

This paper focuses on the noise suppression issue of high-gain extended state observer (ESO) for systems with uncertainty and unknown measurement noise in the active disturbance rejection control (ADRC) paradigm. A special combination of super twisting differentiator (STD) and event-triggered ESO is devised with a designed event-triggered mechanism. Specially, the proposed STD-ETESO incorporates a finite-time switching strategy, which improves the poor transient performance of the STD acting as a pre-filter to prepare relatively smooth signals for the ESO. The locally asymptotic boundedness of the estimation error can be guaranteed by the Input-state-stability (ISS) and existing research for the STD. The effectiveness of the proposed method is validated by a general example and a practical motion control system. Different integral criteria is introduced to evaluate the overall tracking accuracy and energy usage for comparison with existing different ESOs, which reveals the superiority of the proposed STD-ETESO in noise attenuation and disturbance rejection.

Novel Droop-Based Techniques for Dynamic Performance Improvement in a Linear Active Disturbance Rejection Controlled-Dual Active Bridge for Fast Battery Charging of Electric Vehicles

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs.

Optimal and simultaneous synthesis of fractional order QFT controllers and prefilters for non-minimum phase hydro power systems

Abstract Ideally, the operating frequency of the power units, working in parallel, must not deviate from its nominal value. However, due to the dynamic nature of the load, operating frequency varies throughout the day; thus, the control of governors in the prime movers is essential to balance out the demand, and prevent any frequency deviation. To address this, present study investigates the design of optimal and robust quantitative feedback theory based (QFT) controllers and pre-filters for single area load frequency control of hydro power systems. Hydropower system model considered in the present study features a right-hand side zero, thus exhibiting non-minimum phase; these characteristics make the control more challenging. Also, the work investigates the optimal design of the QFT controllers and prefilters simultaneously by automating the loop shaping procedure, which else follows a sequential manual process on Nichols charts. The proposed automation also empowers the control designer to predefine the controller structure. In the present work the design of fractional order QFT controllers and pre-filters is considered. The study also compares the proposed work with the methods proposed in the existing literature as well as the optimal controllers obtained using the time domain indices. The obtained results establishes that the developed fractional QFT controller offers good tracking, stability, and robustness to disturbances when compared to existing approaches. The proposed fractional order QFT controllers offer the elimination of the overshoot and the undershoot from the closed loop response of the system; outperforms the existing controllers in terms of disturbance rejection; offers 0% steady-state deviation, while providing improved phase margin (60.1°) for enhanced stability compared to existing controllers. Also, the proposed QFT-GA attains the ideal values for both the peak complementary sensitivity functions; thus, establishing excellent robustness. Also, the proposed controllers ensure adequate tolerance to frequency deviation in case of load change.