Genetic-Based Active Disturbance Rejection Controller for Super-Heated Steam Temperature Regulation

Owing to ship's high inertia, strong time lag, non-linearity and under-actuated characteristics, as well as sustaining external disturbances frequently, designing a high performance ship tracking controller is still one of the most important research topics of ship motion control. Active Disturbance Rejection Control (ADRC) is proved to be very effective control method because it is independent of mathematical model of the plant and can compensate the internal and external disturbances dynamically. This paper investigates the applications of ADRC in ship motion tracking control and presents the design method of ADRC controller appropriate for ship tracking control. Firstly, ship motion mathematical model is introduced briefly and the linear ADRC (LADRC) is analyzed. Then considering the nonlinear ship track design model as a third order plant after utilizing variable substitution, a LADRC controller is designed by using fourth order linear extended state observer (LESO). At last, the simulation is performed. Simulation results show that the controller has a good tracking performance and disturbance rejection ability and is robust to the ship motion nonlinear characteristic and the external disturbances. The tracking process is fast, smooth and minor energy consumption.

This paper presents a robustness study of an Active Disturbance Rejection Control (ADRC) framework. An experimental verification is carried out on a Two Rotor Aerodynamical System (TRAS). The cross-couplings and highly nonlinear behavior of this complex system limit the performance of classical and linear controllers, widely-used in industrial practice. Additionally, the obtainment of a reliable mathematical model of TRAS is a challenging and tedious job. In ADRC, the unmeasured dynamics and external disturbance, associated with the 2-DOF motion of TRAS, are treated as an additional state variable. It is then estimated by a Nonlinear Extended State Observer (NESO) and compensated in the control signal. The obtained results showed the effectiveness of proposed ADRC-based controller in coping with dynamic and nonlinear variations, commonly seen in aerodynamical systems. It was noticed that due to disturbance estimation and its further cancellation a precise mathematical model of the plant is not required. The ADRC robustness was compared through experiment with other model-free method, i.e. the proportional-integral-derivative controller (PID). Better outcomes in terms of disturbance rejection have been reported for the ADRC approach.

This paper presents a high-precision control scheme based on active disturbance rejection control (ADRC) to improve the stabilization accuracy of an inertially stabilized platform (ISP) for aerial remote sensing applications. The ADRC controller is designed to suppress the effects of the disturbance on the stabilization accuracy that consists of a tracking differentiator, a nonlinear state error feedback, and an extended state observer. By the ADRC controller, the effects of both the internal uncertain dynamics and the external multisource disturbances on the system output are compensated as a total disturbance in real time. The disturbance rejection ability of the ADRC is analyzed by simulations. To verify the method, the experiments are conducted. The results show that compared with the conventional PID controller, the ADRC has excellent capability in disturbance rejection, by which the effect of main friction disturbance on the control system can be weakened seriously and the stabilization accuracy of the ISP is improved significantly.

The speed of nanopositioning system is restricted by its first dominant resonant mode. Various damping techniques have been proposed to suppress the sharp resonance peak and increase the motion speed of nanopositioning system. However, most of the published approaches depend on exact models of the systems, which make the closed-loop systems be sensitive to model errors and external disturbance. In this paper, active disturbance rejection control (ADRC) and integral resonant control (IRC) are combined to restrain the resonance peak, increase the bandwidth and improve the robustness of the closed-loop system. ADRC and IRC add substantial damping for nanopositioning systems, the unique extended stated state observer (ESO) of ADRC guarantees the good disturbance rejection performance of the closed-loop system. Compound ADRC control approach improves the performance of the positioning systems, simulation results confirm the proposed control algorithm.

A tracking control algorithm based on active disturbance rejection controller (ADRC) is proposed to overcome the telescope’s mount fluctuation. The fluctuations are caused by internal and external disturbance when the large aperture telescope runs at ultra-low speed with direct drive. According to the high-precision and high-stability requirements of a large aperture telescope, the ADRC position controller is designed based on segmented arc Permanent Magnet Synchronous Motors (arc PMSMs). The tracking differentiator of ADRC is designed to undergo a transition process to avoid overshoot in the position loop. The speed of target tracking process is observed by the extended state observer and the position information in the system is estimated in real time. The current control variable of the segmented arc PMSM is generated by implementing a non-linear state error feedback control law. The simulation results demonstrate that the proposed control strategy can not only accurately estimate the position and speed of the tracking target, but also estimate the disturbance to compensate the control variables. Experiments showed that the speed error is less than 0.05′ s−1 when using the ADRC, and it can realize high tracking performance when compared with PID controller, which improves the robustness of a large aperture telescope control system.

For high‐order systems, strong nonlinearities, parameter uncertainties and external disturbances pose significant control challenges. Several improved active disturbance rejection controller (ADRC) methods are proposed, including input modified ADRC (MADRC), time‐delay ADRC (DADRC) and Smith‐like ADRC (SLADRC). These improved ADRC methods are systematically compared with conventional proportional integral (PI) control and regular ADRC, focusing on key performance metrics such as tracking accuracy and disturbance rejection capability. The effectiveness of the proposed improved ADRC methods is validated via numerical simulations of a typical superheated steam temperature (SST) system. The integral absolute error (IAE) of ADRC, MADRC, DADRC and SLADRC shows a reduction of around 30, 50, 33 and 35, respectively, relative to that of PI. The outcomes of numerical simulations demonstrate that improved ADRC methods achieve satisfactory control performance, whereas MADRC stands out with the best overall performance. Thus, field application data show that compared with PI, MADRC exhibits significant reductions in key deviation metrics, with the maximum positive deviation decreasing by 33, the maximum negative deviation decreasing by 8, the average positive deviation decreasing by 25, the average negative deviation decreasing by 11 and the variance deviation decreasing by 18.

This manuscript introduces an innovative Maximum Power Point Tracking (MPPT) strategy to improve the efficiency of Wind Energy Conversion Systems (WECS) equipped with Permanent Magnet Synchronous Generators (PMSG) under variable wind conditions. The proposed approach integrates Active Disturbance Rejection Control (ADRC) with the Perturb and Observe (P&O) algorithm, effectively addressing challenges such as external disturbances and fluctuating wind environments. By combining ADRC with P&O control, the system achieves enhanced tracking performance and adaptability.To validate the added value of this approach, we compare it with a traditional P&O strategy combined with Proportional Integral (PI) control. For the PI-based method, controller parameters Kp and Ki are optimized using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to enhance control precision. The Integrated Time Absolute Error (ITAE) objective function is employed to fine-tune these parameters, further optimizing system performance. Our analysis underscores the superiority of ADRC in disturbance rejection and quick adaptability over the PI approach.The proposed strategy is tested under two distinct wind speed profiles—constant and fluctuating—through time-domain simulations in MATLAB/Simulink. Simulation results confirm the superior performance of the ADRC-P&O method, highlighting its effectiveness in maximizing power extraction from wind energy and proving its potential for real-world applications. This study offers a significant advancement in wind energy technology by providing a robust and efficient solution for MPPT in WECS.

Integrated electro-mechanical actuators (EMAs) are widely used in cooperative robots. Their control system has to be carefully designed to achieve a desirable performance. The proportional-integral (PI) control and the active disturbance rejection control (ADRC) are the two commonly used controllers. Compared with the PI, the ADRC shows better performance in terms of disturbance rejection, response speed and overshooting suppression. However, the ADRC utilises numerous parameters and the optimisation of these parameters is a challenge. In this study, the influence of the parameters of β 01 , β 02 , β 03 , β 1 , β 2 on the ADRC performance is deeply analysed. Considering the two non-linear factors of permanent magnet synchronous motor and harmonic transmission in the EMA, a simulation model of the EMA was established. An improved swarm optimisation algorithm was employed to optimise the parameters of the ADRC. A second-order ADRC in speed and position modes was designed. The parameters of the ADRC in these modes were adjusted using improved particle swarm optimisation. ADRC was applied to the EMA field-oriented control system instead of cascade PI, and the simulation and experimental results were compared using the cascade PI control method. When the target speed is to 15 rpm, the speed fluctuation range of the ADRC is about ±0.3 rpm, which is smaller than that of the PI controller’s ±0.5 rpm. Compared with the cascade PI control, ADRC affords a faster response speed, greater robustness and stronger disturbance resistance in the steady-state operation process.

Gas flow facilities are essential to test gas flow sensors, meters and other devices. Pressure control is a key problem in particular for high pressure gas flow facilities. An appropriate control scheme is critical to improve the stability and the speed of the pressure control. This paper presents a linear Active Disturbance Rejection Control (ADRC) scheme for high pressure gas flow facilities with closed-loop pipelines. The control model of the flow facility is identified by a step response method. A reduced-order linear expansion state observer and a related control law are designed to estimate and compensate the total disturbance of the system. The robustness of the ADRC algorithm is compared with a PID control algorithm by Monte Carlo simulation. Control speed and disturbance rejection ability of ADRC are also compared with PID through simulation and gas flow experiments. The results show that, compared with PID, ADRC has a shorter settling time, stronger disturbance rejecting capability and more robust performance. The improved pressure stability has been proved to be beneficial to test gas flow sensors in the facility.

Alpha Beta Gamma Filter for Cascaded PID Motor Position Control

Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. Five-phase motors for propeller propulsion will face various complex sea conditions during actual use, and five-phase motors for electric vehicles will also face various complex road conditions and operating requirements during use. Therefore, as a propulsion motor, its speed control system must have strong robustness and anti-disturbance performance. The use of traditional PI algorithms has problems, such as poor adaptability and inability to adapt to various complex working conditions, but the use of an active disturbance rejection controller (ADRC) can effectively solve these problems. However, due to the significant coupling between the variables of induction motors and the large number of parameters in the ADRC, tuning the parameters of the ADRC is complex. Traditional empirical tuning methods can only obtain a rough range of parameter values and may have significant errors. Therefore, this paper uses ADRC based on genetic algorithm(GAADRC) to tune the parameters of the control and design an objective function based on multi-objective optimization. The parameters to be adjusted were obtained through multiple iterations. The simulation and experimental results indicate that GAADRC has lower startup overshoot, faster adjustment time, and lower load/unload speed changes compared to the empirically tuned PI controller and ADRC. Meanwhile, using a genetic algorithm for motor ADRC parameter tuning can obtain optimal control parameters while the control parameter range is completely uncertain; therefore, the method proposed in this paper has strong practical value.

With the goal of designing a biologically inspired robot that can hold a stable hover under internal and external disturbances. We designed a tailless Flapping-wing Micro Aerial Vehicle (FMAV) with onboard 3D velocity perception. In this way, the wind disturbance caused by the relative motion of the FMAV can be quantified in real time based on the established altitudinal dynamics model. For the rest of the total disturbance, an active disturbance rejection controller is proposed to estimate and suppress those disturbances. In comparison with the traditional PID controller, this proposed approach has been validated. The results show that, in the hovering flight with the internal unmodeled dynamics, the root-mean-square of height controlled is only 2.53 cm. Even with the different weights of loads mounting on the FMAV, the ascending trajectory of flights remains impressively consistent. In the forward flight with the external disturbance, the root-mean-square error of height controlled is 2.78 cm. When the FMAV flies over a ladder introducing an abrupt external disturbance, the maximum overshoot is only half of that controlled by the PID controller. To our best knowledge, this is the first demonstration of FMAVs with the capability of sensing motion-generated wind disturbance onboard and handling the internal and external disturbances in hover flight.

In permanent magnetic synchronous motor (PMSM) speed regulation system, the tracking performance and disturbance rejection property of the system are coupled when using conventional active disturbance rejection control (ADRC) controller, which complicates the parameter setting of the controller. A decoupled ADRC controller with position feedback is proposed in this paper. Extended state observer (ESO) is modified to make disturbance rejection property of the system independent of the proportional coefficient, which affects both the tracking performance and the disturbance rejection property in the conventional ADRC control system. Using the modified ESO, the tracking performance and disturbance rejection property can be adjusted by the proportional coefficient and the bandwidth of ESO respectively, thus simplifies the parameter setting of the controller. Extensive experiments are performed on dSPACE DS1103 to verify the validity and practicality of the proposed algorithm.

Many issues can degrade the electrical drive performance such as cross-coupling, time delay, external disturbances, and parameter variation. The Synchronous Reference Frame (SRF) PI Current Controller (CC) is the most popular control scheme for the motor drive current control due to its simplicity. However, the PI controller does not have an optimal dynamic response due to the reasonably low transient response of the integral parts. Furthermore, the tuning of the PI controller depends heavily on the machine’s parameters. Recently, alternative control schemes such as Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) are studied due to their dynamic performance and disturbance rejection capability, respectively. This paper presents a comparative study between the conventional PI, ADRC, and MPC control schemes applied for Permanent Magnet Synchronous Motor (PMSM) taking into consideration the operational issues of electrical drives.

Active disturbance rejection control (ADRC) is a robust methodology that does not require precise knowledge of the plant. Developed in China by Professor Jingqing Han, it is based on PID control, state observers, and nonlinear functions. Brushed DC motors are known for their low cost and the noise they introduce into control circuits. This paper demonstrates that ADRC can effectively control low-power brushed DC motors using a general nonlinear model and Simulink for tuning. The model is simulated using parameters provided by the manufacturer. An ADRC developed and programmed by the authors in MATLAB is then integrated into the simulation. The controller is tuned, and its performance is verified. Subsequently, the ADRC is implemented on a Raspberry Pi 3 using MATLAB’s support packages and methods developed by the authors. The controller is tested on a Faulhaber 2342L012CR DC motor (12 V/17 W). The results show that it is possible to control the position of the low-power brushed DC motor through simulation-based tuning. The interaction between Simulink and Raspberry Pi 3 enables an optimal control characterized by a fast response, a minimal steady-state error, and no perceptible overshoot. This implementation demonstrates that ADRC is a practical and efficient control method for brushed DC motors.