Parameters Of Active Disturbance Rejection Control Research Articles

Internal Model Principle-Based Extended State Observer for the Uncertain Systems with Nonconstant Disturbances

Existing traditional expansion state observers exhibit good tracking performance for constant and low-frequency disturbances. However, their ability to track non-constant disturbances such as ramp and high-frequency harmonics is inadequate. This paper proposes an extended state observer design method based on the internal model principle. This method achieves precise tracking of non-constant disturbances in the system, effectively addressing the issue of disturbance estimation errors in conventional expansion state observers. When applied to control systems, this approach significantly mitigates or suppresses system vibrations caused by non-constant disturbances, thereby enhancing control accuracy. Furthermore, it demonstrates the stability of the controlled system and the active disturbance rejection controller parameters over a wide range of variations. Simulation results indicate that the ADRC controller based on the proposed observer in this paper offers notable advantages, including high tracking accuracy, strong disturbance rejection capability, and good stability, leading to commendable control performance.

Intelligent ADRC‐based inertia control for offshore wind farm

AbstractThis paper proposes a virtual inertia control strategy for wind farms based on auto disturbance rejection controller with artificial bee colony (ABC) algorithm. First, based on the system frequency dynamic response equation, an active disturbance rejection controller is designed according to the non‐linear feedback control law and the extended state observer to improve the anti‐interference capability of the virtual inertia control system. Second, in order to solve the problem of difficulty in tuning the parameters of the active disturbance rejection control (ADRC), an ABC algorithm based on nectar collection behaviour was proposed to iteratively optimize the parameters of ADRC. Finally, the proposed control method is effectively verified based on Matlab/Simulink. The simulation results show that compared with traditional algorithms, the proposed ABC‐ADRC can effectively control the rotor speed to adjust the frequency of the power grid system, achieve power sharing and adaptive noise elimination, reduce the complexity of parameter settings, not only improve anti‐interference ability, but also enhance the robustness of the system.

Prediction and control strategy based on optimized active disturbance rejection control for AHC system

In this paper, a novel control and prediction algorithm is proposed to solve the problem of decoupling control and response delay in the AHC system of electric winch, which effectively improves the compensation accuracy and response speed of the AHC system. The innovation of the control strategy is mainly as follows: 1) Long short-term memory (LSTM) neural network is used for predicting the ship heave motion. The Bayesian algorithm is used to optimize the hyperparameters to improve the prediction accuracy.2) In the design of the AHC controller, the main speed feedforward is used to directly govern the system's output. The position loop adopts the active disturbance rejection control (ADRC) to improve the anti-disturbance and position-tracking ability of the AHC system.3) For the numerous parameters of ADRC, the chaotic particle Swarm optimization (CPSO) algorithm is used to tune parameters. The simulation results show that the algorithm is effective in improving the robustness and compensation efficiency of the system. Finally, the experiment is carried out on the AHC experimental platform of the full-size winch. The compensation results before and after prediction are compared and analyzed.

Multiscenarios Parameter Optimization Method for Active Disturbance Rejection Control of PMSM Based on Deep Reinforcement Learning

In this article, a multi-scenarios parameter optimization method for active disturbance rejection controller (ADRC) of permanent magnet synchronous motor (PMSM) based on deep reinforcement learning (DRL) is proposed as MSPO-DRL. The parameter setting of nonlinear ADRC has always been one of the difficulties affecting the optimal performance of ADRC, and there will be different optimal parameters under different control requirements. In this article, artificial intelligence algorithm is introduced into the parameter optimization process of ADRC, and the DRL parameter optimization model which can automatically optimize and adjust the ADRC parameters in different application scenarios is constructed so that the ADRC can achieve the best control effect conveniently, and the limitation of current methods has been solved. ADRC is applied to the speed loop of flux weakening control of PMSM for more electric aircraft, and the mathematical model of ADRC in this environment is built above all. The Markov decision process is integrated into ADRC. The interface module and reward function between ADRC and MSPO-DRL are designed. The concept of ADRC control scenario is defined and integrated into the concept of Markov decision process to improve the generalization of DRL. Then the MSPO-DRL model is established, and the deep deterministic gradient strategy is used as the gradient descent strategy to converge the parameters optimization. After the model learning is completed, different environmental conditions are randomly selected for simulation and experiments to verify the optimization effect and generalization performance of the algorithm. Optimizations which are carried out by the heuristic algorithms are used for comparisons, and the superiority and feasibility of the proposed algorithm is verified.

Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies

This study aims to achieve rapid and stable control of quadrotor unmanned aerial vehicles’ (UAVs) attitude by using an Active Disturbance Rejection Control (ADRC) controller. Addressing the challenge of numerous and complex ADRC parameters, optimization algorithms are employed for parameter tuning. This paper draws on the group mechanism of the Ant Colony Optimization (ACO) algorithm and innovatively introduces population search into the Beetle Antennae Search (BAS) algorithm. The refined algorithm is then applied to tune the ADRC parameters, reducing complexity and human intervention while enhancing intelligence and efficiency. The advanced optimization algorithm exhibits an exceptional global optimization capacity, convergence speed, and stability. Ultimately, flight simulation and experimental results suggest that the optimized ADRC controller demonstrates superior control and antidisturbance capabilities.

A Summary of Parameter Tuning of Active Disturbance Rejection Controller

Background: ADRC (active disturbance rejection controller) technology is a new practical technology that does not rely on the mathematical model of the controlled object and has strong robustness. It integrates the essence of modern control theory and classical proportion integral derivative theory and has good engineering application prospects. But with the research and development of the adaptive disturbance rejection controller, the problem of many parameters and the difficulty to adjust also arises. Objective: To act as a guide for future studies on the enhancement of ADRC parameter setting, to support the growth of ADRC technology, and to promote the effective use of the technology in other control fields. Method: The basic concepts and principles of ADRC control, the research methodologies for ADRC parameter tuning, and the research progress for ADRC parameter tuning in each direction are all introduced in this paper. The benefits and drawbacks of each method are then compiled, and a potential course of future development is suggested. This information is expected to serve as a guide for future studies on the enhancement of ADRC parameter tuning. Conclusion: The parameter tuning of the ADRC is a complex adjustment process. At present, the mainstream parameter tuning methods include the empirical method, the bandwidth method, the intelligent algorithm tuning method, and the time scale tuning method. Among them, the empirical method demands the debugging personnel to adjust conform to the accumulated experience, and the adjustment process is cumbersome; The bandwidth method needs to go through a lot of tedious calculations to determine the control parameters according to the model of the system, and the generality is poor; and the use of intelligent algorithms to tune ADRC parameters has become the most widely used method for tuning parameters.

Improved active disturbance rejection control with the optimization algorithm for the leg joint control of a hydraulic quadruped robot

To enhance the joint control performance in hydraulic quadruped robots, active disturbance rejection control was used in the leg joint controller design of a hydraulic quadruped robot in combination with the self-growing lévy-flight salp swarm algorithm in this paper. First, the robot-leg structure of the hydraulic system model was built to analyze system operation details in terms of the mathematical construction. Second, the self-growing lévy-flight salp swarm algorithm was introduced. Then, the active disturbance rejection control parameters required were defined based on the composition and principle of the third-order active disturbance rejection control model. Third, the system evaluation function ITSE was selected, and the parameter tuning problem was converted into an algorithm optimization problem. Finally, the joint system of the hydraulic quadruped robot was taken as the research object, the self-growing lévy-flight salp swarm algorithm was added to the active disturbance rejection control to tune parameters, and this paper compared three different algorithms in the same environments to show the good control ability of the proposed method. To comprehensively show the performances of robot joint systems controlled by the proposed controller, the temporal response results, the frequency response results, the sawtooth response results, the ramp response, and the random response were displayed. All results reveal the effectiveness and excellent performance of the proposed controller in joint systems of a hydraulic quadruped robot.

An ADRC Parameters Self-Tuning Controller Based on RBF Neural Network for Multi-Color Register System

To improve the control precision of the nonlinear register system for flexographic printing, a feedforward active disturbance rejection control (ADRC) parameter self-tuning decoupling control strategy based on radial basis function (RBF) is proposed to address the existence of coupling interference and multiple working conditions. Firstly, according to the structure of the flexographic printing equipment system and registration principle, a nonlinear mathematical model of the global registration system is established and linearized using the small deviation method. Secondly, the decoupled controller of the register system is designed by integrating feedforward control, ADRC, and RBF, in which the feedforward control is used to eliminate the registration errors caused by modeled disturbances, the ADRC performs the estimation and compensation of unmodeled disturbances, and the RBF realizes the self-tuning of the ADRC controller parameters. Finally, different operating conditions are simulated to compare and verify the control performance of the proposed controller. Simulation results show that the designed controller has a better performance compared to traditional PID and ADRC control, and its register error peak is reduced by about 32% compared to ADRC, achieving the high accuracy control of a multi-color register system.

Improved Grey Wolf Optimizer Tuned Active Disturbance Rejection Control for Ship Heading

The ship heading control is very important to the shipping industry. The Active Disturbance Rejection Control (ADRC) method is ideal for ship heading control, but it is highly correlated with parameters. In this brief, we employed the chaotic mapping and fractional derivative update method to enhance the Grey Wolf Optimizer (GWO), namely Improved GWO (IGWO). The IGWO is proposed to tune the parameters of ADRC adaptively, then the IGWO-tuned ADRC is utilized to control the ship heading. As the comparison, four other optimizers are also employed to tune the parameters of ADRC. Five different disturbance situations are considered to validate the stability of these tuning methods. The robustness of the proposed method for ship parameters perturbation is also analyzed by Monte Carlo experiments. The results indicate that the performance of proposed methods is superior to others.

Realization of Control Algorithm for Vehicle Optoelectronic Tracking Platform Based on Sliding Mode Control and Active Disturbance Rejection Control Optimized by Differential Evolution Algorithm

In order to improve the tracking ability of the vehicle mounted photoelectric tracking platform, and make its tracking system has the characteristics of rapid response, small overshoot, high-precision position tracking, strong anti-interference and good robustness, In this paper, a joint active disturbance rejection control (ADRC) and sliding mode control (SMC) method is proposed, which can effectively improve the tracking ability of the equipment. Firstly, the mathematical model of the DC motor is analyzed, and then the sliding mode control based on the linear extended state observer (LESO) and the third-order tracking differentiator is established as the speed loop control of the system. Finally, the parameters of the nonlinear ADRC which built control system,s position loop are optimized by the improved differential evolution (DE) algorithm. The speed loop simulation results show that the proposed algorithm has better control effect than PID control algorithm, Robust control algorithm, and ADRC control algorithm; The simulation of position loop demonstrates that the performance of the controller optimized by DE algorithm is better than the parameter tuning based on experience. The experimental results illustrate that the proposed control algorithm can ensure that the Optoelectronic Tracking equipment can track the target which 3km away with 0.1 mrad accuracy.

Active Disturbance Rejection Control Based on Deep Reinforcement Learning of PMSM for More Electric Aircraft

In this article, an active disturbance rejection controller (ADRC) based on deep reinforcement learning (DRL) algorithm is proposed to be used in the flux weakening control (FWC) system of motors for more electric aircraft. Artificial intelligence algorithm is introduced into ADRC motor control system for the first time, and DRL is designed as the automatic tuning for the parameters optimization of ADRC. The interface module scheme is proposed to realize the conversion between the relevant quantities of the control system and the DRL Agent according to the characteristics of ADRC. The parameters are optimized in the form of parameter modification, and a new DRL-ADRC control framework is proposed which can avoid being trapped into local optimum. The ADRC model designed for the speed loop of FWC system are first introduced. An interface module is subsequently built to enable DRL to interact with the FWC system automatically. DRL agent is trained to optimize the internal parameters of ADRC, which have the characteristics of large quantities, weak sensitivity and strong coupling. Deep deterministic policy gradient is used as the strategy of DRL, which can quickly determine the descent gradient and converge the multiobjective optimization problem. Simulation and comparison with classical heuristic algorithms and disturbance rejection methods are carried out to show the superiority of DRL. The feasibility and effectiveness of the proposed control method are verified by experiments on an aerospace motor for MEA.

Parameters optimisation on active disturbance rejection control of a bearingless induction motor

ABSTRACT Aiming at the problem that the parameters tuning depends on empirical adjustment and it is difficult to obtain the optimal solution in the active disturbance rejection control (ADRC) system of a bearingless induction motor (BIM), a fusion control strategy based on improved particle swarm optimisation (IPSO) and simulated annealing (SA) algorithm is proposed. Based on the orientation of the air gap magnetic field, the first-order torque ADRC and the second-order levitation ADRC are designed for the rotating and suspension parts of the BIM, respectively. The parameters of the ordinary particle swarm optimisation algorithm are improved to obtain the fast optimisation ability; The Metropolis criterion of the simulated annealing algorithm is combined to improve the accuracy of the final solution. Meanwhile, several groups of test functions are used to test IPSO-SA and verify the stability as well as the accuracy of the algorithm. Finally, the parameters of ADRC are adjusted by IPSO-SA, and the obtained parameters are simulated by Matlab/Simulink operating environment and verified by experiments on the experimental prototype. Both simulation and experimental results show that the proposed control strategy can not only obtain good speed characteristics and improve the robustness of the system but also effectively realise its high-performance suspension.

Deep Deterministic Policy Gradient and Active Disturbance Rejection Controller based coordinated control for gearshift manipulator of driving robot

In order to improve the shift precision of the gearshift manipulator, a coordinated control method for the gearshift manipulator based on the Deep Deterministic Policy Gradient (DDPG) and Active Disturbance Rejection Controller (ADRC) is proposed. Firstly, the kinematic model and dynamics model of the gearshift manipulator are established. Secondly, a coordinated control strategy is proposed to solve the target rotation angle of the servo motor, so as to deal with the nonlinear trajectory problem caused by the mechanical decoupling strategy. Then, an ADRC-based controller and a DDPG-based parameter adjuster are proposed. A DDPG model is established to adaptively adjust the control parameters of ADRC through the target rotation angle, actual rotation angle and rotation angle error. Finally, the theoretical stability proof and experimental verification of the proposed method are conducted. The test results show that the mean absolute error of the proposed method is 34% and 18% lower than that of ADRC and fuzzy PID methods. It shows the effectiveness of the method.

Clinical Trajectory control for lower Knee rehabilitation using ADRC method

The design and study of the Active Disturbance Rejection Control (ADRC) approach for control and disturbance rejection for lower knee rehabilitation utilizing a residential exoskeleton system are provided in this paper. Linear ADRC (LADRC) and Nonlinear ADRC (NADRC) are two controllers that are considered for control purposes based on the structure of ADRC. The contrasts between them are thoroughly discussed in this work. The LADRC is made up of a tracking differential (TD), a linear proportional derivative (LPD) controller, and a linear extended state observer (LESO), whereas the NADRC is made up of the same LESO but with a nonlinear PD (NPD) and a modified optimized TD (MTD). In terms of robustness against the ability to reject applied disturbances, a comparison of LADRC and NADRC has been done. The fundamental challenge with ADRC is that its constituent parameters must be fine-tuned. Grey Wolf Optimizer (GWO) has been proposed for tuning the parameters of ADRC to have the least amount of error variation in order to improve the controlled system's dynamic performance. When a prescribed disturbing torque is applied, the results of a MATLAB simulation show that the LADRC has greater disturbance rejection capabilities than the NADRC.

A synthesis method for first-order active disturbance rejection controllers: Procedures and field tests

Active disturbance rejection control (ADRC) is getting explosive attention with many advantages. This paper proposes a synthesis method for the first-order ADRC based on the existing PI controller. Firstly, the relationship between the first-order ADRC and PI controller is derived. Then the initial parameters of ADRC can be obtained directly from the existing PI parameters and the synthesis procedure is summarized. Moreover, the parameter constraints to obtain better control performance are acquired based on theoretical analyses and ADRC parameters can be optimized based on these parameter constraints. The effectiveness of the proposed ADRC synthesis method and the parameter optimization method is validated by simulations and comparative experiments. Finally, the proposed methods are applied to the secondary air system of a power plant and running data indicate that ADRC can improve the control performance evidently.

Application of the Gray Wolf Optimization Algorithm in Active Disturbance Rejection Control Parameter Tuning of an Electro-Hydraulic Servo Unit

A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, and the electro-hydraulic servo unit encounters shock, vibration, and other external interference when working, which seriously affect the stability of the valve-controlled hydraulic cylinder system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into the electro-hydraulic servo control. However, there are many ADRC parameters, and it is difficult to set these only with expert experience. Therefore, we propose applying the gray wolf optimization algorithm (GWO) to the ADRC, to auto-tune the parameters and find the optimal solution. In addition, the advantages of the GWO in ADRC parameter tuning are proven and analyzed. The simulation and experimental results showed that the GWO algorithm had a faster mean time for parameter tuning and the smallest fitness value (integrated time and absolute error), compared to the particle swarm optimization algorithm and genetic algorithm. Moreover, a valve-controlled cylinder system, after parameter tuning by the gray wolf optimization algorithm, could accurately adjust the parameters of the auto-disturbance rejection controller, with a faster response speed, smaller overshoot, and better anti-disturbance ability.

Managing the Handling-Comfort Trade-Off of a Quarter Car Suspension System using Active Disturbance Rejection Control and Vyshnegradsky Equation

In this paper, we tackle the handling-comfort conflicting problem of a quarter-car system using Active Disturbance Rejection Control (ADRC). ADRC parameters are tuned using Vyshnegradsky equations which determine the stability criteria of a third order system. To do this, a multi-objective optimization procedure for selection of ADRC observer coefficients is formulated using a genetic algorithm. Suspension deflection and sprung mass acceleration responses are tested to a random road disturbance input. Simulation results show that the compromised solution between handling and comfort can be achieved by introducing the sprung mass acceleration into the feedback loop of ADRC. Using this approach allows for improving the issue of comfort up to 50 percent with just 10 percent worse performance of the issue of handling.

Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

Study on Active Disturbance Rejection Control of a Bearingless Induction Motor Based on an Improved Particle Swarm Optimization–Genetic Algorithm

To overcome the limitations that active disturbance rejection control (ADRC) system of a bearingless induction motor (BIM) has difficulty in tuning parameters depending on experience to select parameters, an ADRC strategy based on improved particle swarm optimization-genetic algorithm (IPSO-GA) is proposed. Based on the orientation of the air-gap magnetic field, the first-order and the second-order ADRC are, respectively, designed for the BIM rotation and suspension parts according to the different order of the BIM system. Then, the parameters of the basic particle swarm optimization algorithm are optimized by considering the characteristics of the basic particle swarm algorithm parameters, and the crossover and mutation operations are introduced to enhance global search capability. Meanwhile, through the performance test based on the test function, the performance of IPSO-GA is verified, and the parameters of ADRC are adjusted by IPSO-GA. In addition, this strategy is analyzed with simulation in MATLAB/Simulink and verified on an experimental prototype. Both simulation and experimental results show that the proposed strategy not only effectively improves the starting performance and antidisturbance ability of the BIM but also reduces the maximum radial offset of the rotor and improves the suspension precision of the motor.

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle (UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization (MSPIO) algorithm is employed. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the basic Pigeon-Inspired Optimization (PIO), and an improved PIO algorithm CMPIO are compared. In addition, the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative (PID) control system and the non-optimized ADRC control system. The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.