Fuzzy active disturbance rejection control-based pressure tracking for fuel cell hydrogen supply systems

In this study, a type of direct-drive gearshift system integrated into a motor-transmission coupled drive system is introduced. It used two electromagnetic linear actuators (ELAs) to perform gearshift events. The adoption of ELAs simplifies the architecture of the gearshift system and has the potential to further optimize gearshift performance. However, a number of nonlinearities in the gearshift system should be investigated in order to enhance the performance of the direct-drive gearshift system. An active disturbance rejection control (ADRC) method was selected as the principal shifting control method due to the simple methodology and strong reliability. The nonlinear characteristics of the electromagnetic force produced by the ELA were subsequently reduced using the inverse system method (ISM) technique. The ADRC approach also incorporated an acceleration feedforward module to enhance the precision of displacement control. The extended state observer (ESO) module used a nonlinear function in place of the original function to improve the ability to reject disturbances. Comparative simulations and experiments were carried out between the ADRC method and improved ADRC (IADRC) method. The outcomes demonstrate the effectiveness of the designed control method. The shift force fluctuates less, and the shift jerk decreases noticeably during the synchronization procedure. In conclusion, combined with the optimized IADRC method, the direct-drive gearshift system equipped with ELAs shows remarkable gearshift performance, and it has the potential to be widely used in motor−transmission coupled drive systems for EVs.

Modern greenhouses need a certain equipment to achieve the expected environmental temperature in a short time while saving energy. To achieve greenhouse temperature management and energy savings through intelligent control, a greenhouse mechanism model is first built to study the controller design for a greenhouse located in Yiyang city in this paper. Then, the model accuracy is verified by experimental data. Based on the verified model, two intelligent control technologies are proposed, namely, active disturbance rejection control and fuzzy active disturbance rejection control. Methods are adopted to adjust skylight opening and thermal air conditioning for greenhouse temperature control and energy savings. In 60 hours of continuous working time, fuzzy active disturbance rejection control takes 10 hours less than active disturbance rejection control to make the greenhouse temperature reach the ideal steady state, and the temperature overshoot is reduced by 60%. Through analysis, the proposed fuzzy active disturbance rejection control method for greenhouse temperature management can achieve 15% energy savings. The proposed intelligent control technology can also be applied to temperature management for real greenhouses.

This paper presents a simplified active disturbance rejection control (ADRC) method for implementing the DC capacitor voltage in Z-source constant. Active disturbance rejection control method is the development of PID control. It is suit ab le for the inverter with variable input. A simplified ADRC method is used in this paper to reject the disturbance from variable input in Z-source inverter. In its ESO module part, Kalman filter is used to estimate the state and the disturbance, which can approach the true value at the extreme, and reduce the computation time. The simulation result shows the control method can achieve the control requirement satisfactory.

Considering the internal and external disturbances of the direct‐driven wind energy conversion systems, i.e. multi‐variable coupling, non‐linear characteristic and modelling error, and the environmental disturbances, an adaptive sliding mode observer with active disturbance rejection control (ADRC) method for a direct‐driven permanent magnet synchronous generator (PMSG) based wind power conversion system is proposed to track the maximum power point precisely. First, adaptive sliding mode observer based on phase‐locked loop can effectively observe the rotor position information of PMSG, and weak the inherent chattering of traditional sliding mode. Second, ADRC can enhance the anti‐disturbance ability when the system is under linear state feedback. Finally, the simulation results show that this method can estimate the rotor position exactly, and the system has the strong robustness and the optimum power tracking performance.

In the position control of the mechanical arm of an iron roughneck (MAIR), a controller with high responsiveness, high accuracy, and high anti-interference capability is necessary. An MAIR consists of two proportional-valve-controlled single-extension (PVCSE) hydraulic cylinders, and a traditional proportional–integral–derivative (PID) controller cannot easily achieve the accuracy and robustness requirements of the hydraulic cylinders. In this paper, a three-dimensional fuzzy active disturbance rejection controller (TF-ADRC) is proposed for an MAIR, which adds a three-dimensional fuzzy module to a classical active disturbance rejection controller (ADRC) to adjust the controller output according to the tracking of differential deviation, deviation change rate, and deviation change acceleration rate. Firstly, the trajectory planning of the MAIR was carried out using the quintic polynomial interpolation method to improve the smoothness of the target trajectory. Then, the reliability of the established model was verified by experiments. Finally, the comprehensive performance of a PID controller, fuzzy PID controller, ADRC, and TF-ADRC were compared based on the AMESim-Simulink model. The system with the TF-ADRC exhibits higher position control accuracy and better anti-interference capability than the system with a PID or Fuzzy PID controller, and accuracy is higher compared with the common ADRC.

Electromechanical actuators (EMA) are becoming more and more widely used. As the core technology of EMA, servo control technology determines their performance. In this paper, an active disturbance rejection control (ADRC) method with an improved extended state observer (ESO) is proposed to design a cascade controller of EMA based on permanent magnet synchronous motor (PMSM). The mathematical model of PMSM in a two-phase rotating coordinate system is established, then it is decoupled by an id=0 current control method to realize the vector control of the motor. In a three closed-loop vector control system, a PID controller including current loop, speed loop and position loop is designed. To solve the problems caused by measurement noise, the filter link and system are modeled as a whole, and an improved ESO is constructed. On this basis, an ADRC controller of the speed loop and position loop of PMSM is designed and simulated based on Simulink. Based on the physical test platform, a load step test and load disturbance test of ADRC are completed. The results show that, in comparison to the PID method, the ADRC method shortens the response time by 25% on average, and reduces the overshoot by 60% on average. So, it can be concluded that ADRC has good static and dynamic performance, which has a good guiding role for engineering practice.

The friction force inside the Bowden cable will lead to the gap, hysteresis and non-linearity in the force transmission system, The fabrics wrapped around the limbs has the hysteresis and internal friction characteristics of polymer, which will cause the hysteresis and loss of the force generated by the system. In this paper, the static Coulomb friction math model of the Bowden cable and the finite element model of the fabrics are established for analysis, and the force compensation obtained is added to the control system. Then, based on the model, a fuzzy Active Disturbance Rejection Control (ADRC) method is proposed, which is compared with the traditional PID control method through simulation experiments. The simulation results show that the force tracking error is significantly reduced after the mixed force compensation of Bowden cable and fabrics, and the force tracking error is reduced by 1.96% after the use of fuzzy ADRC.

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.

This chapter deals with the active disturbance rejection control (ADRC) method to the lateral control of small air-to-ground missiles for helicopters. The main difficulties that arise in lateral control design of small missiles are related to the highly uncertain, nonlinear aerodynamic forces and force movements, thus it is difficult to obtain an accurate dynamic model. Besides, the huge variations of dynamic pressure make controller parameters, and are difficult to adapt to the whole flight envelope. To handle them, the design of the controller is based on ADRC, a nonlinear control design approach which is based on the extended state observer (ESO), and can estimate and compensate the uncertainty of the system, including structural errors, random, and other system errors. Six-degree freedom equations are established for the lateral channel, the equations are linearization with small disturbance theory, then the aerodynamic derivatives are obtained through wind tunnel test, the dynamic model is made and the aerodynamic features are analyzed. One-order ESO equations of lateral channel are deduced, the controller of the lateral channel which is based on the ESO is designed and parameters are adjusted, and finally, semi-physical simulations are made. Through the comparison with classical proportion integral differential (PID) controller, the controller based on ADRC has strong robustness and it could track control command rapidly and accurately.

This study investigates the impact of time-varying jet reaction forces on ship-cleaning manipulators, focusing on joint torque fluctuations, high-frequency chatter, and the deterioration of end-effector positioning accuracy. An enhanced active disturbance rejection control (ADRC) method is proposed, which integrates jet reaction force modeling, joint torque feedforward compensation, and improvements to both the extended state observer (ESO) and the nonlinear state error feedback (NLSEF). Simulation results show that the enhanced ADRC reduces the joint stabilization time from 10 to 2 seconds, suppresses over 70% of the initial torque spikes, and decreases the ESO observation errors by more than 60%. Compared with the classical sliding mode control (SMC), the proposed method effectively eliminates high-frequency chattering phenomena and reduces control signal aggressiveness during the startup phase. This method improves trajectory tracking accuracy under strong disturbances while smoothing the control output and significantly reducing high-frequency joint chatter. These results confirm that the enhanced ADRC method improves stability and responsiveness, providing a robust framework for the reliable operation of ship-cleaning manipulators in high-disturbance environments.

In the paper simulation research of speed control of electrical drive with mechanical system revealing elasticity and backlash is presented. The method of Active Disturbance Rejection Control (ADRC) was applied as the speed controller. Parameters of the ADRC subsystem were found by Particle Swarm Optimization method. The ADRC method was compared with 2DOF-PID controller, which was treated as a reference control structure, in conditions of simple scenario with step changes of disturbance (load torque) and reference signal. Resulting waveforms and numeric control quality factors are presented and followed by conclusions.

BTT autopilot design for agile missiles with aerodynamic uncertainty

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 paper presents an active disturbance rejection control (ADRC) method based on radial basis function (RBF) neural network applied to current tracking control of active power filter (APF). It is difficult to accurately model the active power filter system, and the uncertainty of the nonlinear system brings difficulties to the system analysis and control. In this paper, under the ADRC framework, a tracking differentiator (TD) is introduced to obtain the differential information of the reference signal and a nonlinear extended state observer (ESO) is constructed to estimate the state variables and the total disturbance. The nonlinear ESO realizes the feedforward compensation of disturbance, and the state feedback adopts the PD form. Further, the RBF neural network is designed to estimate the unknown parameter b of the system. The simulation results show that the proposed control method has good steady-state and dynamic performance.

An active disturbance rejection control (ADRC) method is proposed for configuration maintenance of an electromagnetically controlled spacecraft cluster. In this paper, the stability of the ADRC controller is analyzed. First, the ADRC controller is introduced. Furthermore, a Lyapunov function is constructed, and according to the positive definiteness of Lyapunov function matrix, a sufficient condition for ADRC controller is derived. Finally, numerical simulations are performed to verify the effectiveness of the controller, and the controller is confirmed to be Lyapunov asymptotically stable.