Steam generator water level SMC-ADRC cascade control based on improved power reaching law

This paper presents an in-depth study of the application of Active Disturbance Rejection Control (ADRC) in the high-precision motion control system of the “WeHoST” 2.5-meter large-aperture telescope using a Programmable Multi-Axis Controller (PMAC). The goal is to enhance the telescope's tracking accuracy and stability under complex environmental conditions. In response to disturbances encountered during the telescope's observation process, an optimized solution based on ADRC was designed and implemented. At first, based on ADRC theory, a controller architecture was constructed, incorporating an Extended State Observer (ESO) and a Nonlinear State Error Feedback (NLSEF) control law. Subsequently, the key parameters of the controller were determined according to the specific parameters and performance requirements of the “WeHoST” telescope to ensure the controller's fast response and stability. The ADRC was then implemented in the PMAC control system, and the telescope's position was monitored in real-time using high-precision encoders. Experimental data show that the positioning accuracy of the “WeHoST” telescope was significantly improved after the introduction of ADRC. In dynamic tracking tests, the tracking error decreased from 0.01817” to 0.01261”, a reduction of approximately 30.59%. The PP(Peak to Peak) value decreased from 0.04585” to 0.03201”, a reduction of approximately 30.18%, significantly enhancing the telescope's tracking accuracy and stability. The study successfully applied ADRC to the main axes of the “WeHoST” telescope. demonstrating its effectiveness in improving the tracking accuracy and stability of the telescope. It provides valuable insights and references for instruments requiring high-precision motion control.

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.

The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy.

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.

Aiming at the problem that the suspension force under disturbance of the permanent magnet slice rotor is difficult to be accurately detected, a new displacement controller based on the active disturbance rejection control is proposed. Firstly, the mathematic models of torque and suspension forces of the bearingless permanent magnet slice motor (BPMSM) are deduced. Secondly, the active disturbance rejection (ADR) controller is constructed. Thirdly, the BPMSM direct control system based on the ADR controller is built and the simulation is carried out, the correctness of the mathematic model and the control algorithm are verified. Finally, the displacement controller is used for a 4 kW prototype. Compared with the traditional PI controller, the floating stability time and the disturbed recovery time of the slice rotor are both decreased, the vibration displacement of rotor in stable state is reduced by 44%.

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.

PurposeThis paper aims to study a disturbance rejection controller to improve the anti-interference capability and the position tracking performance of airborne radar stabilized platform that ensures the stability and clarity of synthetic aperture radar imaging.Design/methodology/approachThis study proposes a disturbance rejection control scheme for an airborne radar stabilized platform based on the active disturbance rejection control (ADRC) inverse estimation algorithm. Exploiting the extended state observer (ESO) characteristic, an inversely ESO is developed to inverse estimate the unmodeled state and extended state of the platform system known as total disturbances, which greatly improves the estimation performance of the disturbance. Then, based on the inverse ESO result, feedback the difference between the output of the tracking differentiator and the inverse ESO result to the nonlinear state error feedback controller (NLSEF) to eliminate the effects of total disturbance and ensure the stability of the airborne radar stabilized platform.FindingsSimulation experiments are adopted to compare the performance of the ADRC inverse estimation algorithm with that of the proportional integral derivative controller which is one of the mostly applied control schemes in platform systems. In addition, classical ADRC is compared as well. The results have shown that the ADRC inverse estimation algorithm has a better disturbance rejection performance when disturbance acts in airborne radar stabilized platform, especially disturbed by continuous airflow under some harsh air conditions.Originality/valueThe originality of this paper is exploiting the ESO characteristic to develop an inverse ESO, which greatly improves the estimation performance of the disturbance. And the ADRC inverse estimation algorithm is applied to ameliorate the anti-interference ability of the airborne radar stabilization platform, especially the ability to suppress continuous interference under complex air conditions.

In this paper, the problem of attitude tracking control for a class of vertical take-off landing (VTOL) aircraft in the presence of uncertain disturbances is addressed. The active disturbance rejection control (ADRC) method is utilized to address uncertain disturbances, while the radial basis function (RBF) neural network is considered for handling unknown nonlinear dynamics. By incorporating a modified tracking differentiator, a nonlinear state error feedback controller, and an extended state observer, the disturbance observation problem in the aircraft system is solved. These disturbances, seen as a combined disturbance in the aircraft system, are counteracted through the extended state observer. Furthermore, the unknown nonlinear dynamics within the aircraft system are approximated using the RBF neural network. Subsequently, the paper suggests adaptive ADRC strategies based on neural network and formulates adaptive control laws to estimate unknown parameters. The Lyapunov stability theory proves the effectiveness of the proposed control strategies. The simulation results show that the attitude tracking control problem of VTOL aircraft can be achieved under the proposed control strategies and adaptive control laws.

The conventional proportional-integral (P-I) controller for the steam generator water level has no feedforward action at low powers because of the large flow errors, which cannot effectively reduce the swell and shrink phenomena. The steam generator water level is the sum of three water level quantities that are induced by the mass capacity effect, the swell and shrink phenomena, and the mechanical oscillations. Each individual quantity of the three water levels is not measurable. The water level quantity due to the mass capacity (water inventory) is important for cooling of the primary side, which is an essential function of steam generators. Therefore, the flow errors and the unmeasurable water level quantity are estimated and then used to generate the control input (feedwater flow rate). Under practical situations, the proposed controller is predicted to have a better performance than the conventional P-I controller.

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method for a steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are.

For optimal control of synchronous machine, chattering phenomenon will appear if traditional slider control is adopted because permanent magnet synchronous machine is a complex nonlinear time-dependent system with strong coupling of current and rotational speed to cause the deterioration of system control performance with load or load disturbance. In this article, based on the mathematical model of permanent magnet synchronous machine, a control system for it, which combines sliding mode control and active disturbance rejection control, is proposed to improve the dynamic performance and robustness of control system. In the control system, sliding mode control is adopted to control the inner current of machine and active disturbance rejection control is adopted to control the outer speed. The load disturbance of system is also estimated and offset. The results of matlab simulation show that the control system can eliminate serious chattering phenomenon existing in sliding mode control, improves the robustness of system for load and system parameter disturbance as well as has great dynamic and static performance.

In a nuclear power plant, the water level in the Steam Generator(SG) is one of main causes that shutdown the reactor, this problem has been of great concern for many years as the SG is a highly nonlinear system showing inverse response dynamics. For controlling the SG water level at a certain range, Adaptive Neuro-Fuzzy Inference System (ANFIS) PID controller is designed. It is based on the adaptive Nero-Fuzzy Inference System, using error and error change rate as inputs of fuzzy pid. Neural network has ability to self-study, so fuzzy rules of ANFIS PID can adjust adaptively on line. Applying ANFIS PID controller on the steam generator, comparing with cascade pi controller and fuzzy pid controller, result shows that ANFIS PID can control the level of SG more accurately and safely.

Nowadays, Dynamic Positioning (DP) is applied to various tasks such as subsea pipeline laying and the requirements for the positioning performance in marine operations are higher and higher. The main objective of this paper is to design a DP controller based on the Active Disturbance Rejection Control (ADRC) to solve the problems of long response time, large overshoot and low positioning accuracy in ship positioning. Firstly, the mathematical models of the ship and environmental disturbances are established. Secondly, the basic principle of ADRC is described. Meanwhile, stability analysis of the control system is introduced. Thirdly, ADRC is improved by the fal function filter and phase prediction method which solve the problem of dither and phase delay in tracking differentiators. Finally, simulations are carried out to verify the performance of the designed ADRC and the improved ADRC. Several simulation results show that the designed ADRC can realize the fixed-point control of the ship, which effectively solves the problems of long response time, overshoot and positioning accuracy, and compared with the traditional ADRC, the improved ADRC can reduce the error of straight track control, which indicates that ADRC can meet the requirements for the positioning performance and has a strong application value.

To address time delay and noise problems in control systems, in this study, we integrated an extended state filter for signal filtering into an active disturbance rejection control (ADRC) system and derived an improved ADRC approach. In addition to the active anti-disturbance and active tracking estimation functions of the existing ADRC, the proposed approach also includes active filtering and active advance prediction functions, which can filter out the effect of measurement noise on system state observation while reducing the delay between the system control output and the detection of the sensor input. We verified through an evaluation in a simulation environment that the proposed approach may be expected to achieve improved control accuracy and increase the stability of closed-loop control systems.

Usually, the order of active disturbance rejection control (ADRC) is equal to the relative order of the plant. To improve the control performance, a robust reduced-order method for ADRC is investigated in this paper. Firstly, frequency domain analysis shows that the lower-order extended state observer (ESO) has a smaller disturbance estimation error, so disturbance attenuation capability can be improved by reducing the order of ADRC. However, using only reduced-order ADRC will worsen the robustness of closed-loop systems. Therefore, a robust ADRC method based on a modified noise reduction disturbance observer (MNRDOB) is proposed. The main role of the MNRDOB is to improve the control performance of the closed-loop system by modifying the structure of the controlled object. In addition, the robust stability of the closed-loop control system based on the MNRDOB is discussed. Moreover, some simulations are used to demonstrate the robustness and noise suppression effects of the compound control method reduced-order ADRC with MNRDOB, and the parameter tuning method for the MNRDOB to improve the robustness of the system is given. Finally, some experiments on speed control of a one-dimensional gimbal are performed, and the results show that the proposed method is excellent in overshoot, tracking accuracy, and disturbance attenuation.