scholarly journals Optimal Fractional-Order Active Disturbance Rejection Controller Design for PMSM Speed Servo System

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 262
Author(s):  
Pengchong Chen ◽  
Ying Luo ◽  
Yibing Peng ◽  
Yangquan Chen
Keyword(s):  
Fractional Order ◽  
Disturbance Rejection ◽  
Pid Controller ◽  
External Disturbance ◽  
Servo System ◽  
Stable Region ◽  
Speed Tracking ◽  
Active Disturbance Rejection ◽  
Load Disturbance ◽  
Tuning Strategy

In this paper, a fractional-order active disturbance rejection controller (FOADRC), combining a fractional-order proportional derivative (FOPD) controller and an extended state observer (ESO), is proposed for a permanent magnet synchronous motor (PMSM) speed servo system. The global stable region in the parameter (Kp, Kd, μ)-space corresponding to the observer bandwidth ωo can be obtained by D-decomposition method. To achieve a satisfied tracking and anti-load disturbance performance, an optimal ADRC tuning strategy is proposed. This tuning strategy is applicable to both FOADRC and integer-order active disturbance rejection controller (IOADRC). The tuning method not only meets user-specified frequency-domain indicators but also achieves a time-domain performance index. Simulation and experimental results demonstrate that the proposed FOADRC achieves better speed tracking, and more robustness to external disturbance performances than traditional IOADRC and typical Proportional-Integral-Derivative (PID) controller. For example, the JITAE for speed tracking of the designed FOADRC are less than 52.59% and 55.36% of the JITAE of IOADRC and PID controller, respectively. Besides, the JITAE for anti-load disturbance of the designed FOADRC are less than 17.11% and 52.50% of the JITAE of IOADRC and PID controller, respectively.

scholarly journals Fractional-Order PID Controller for Active Power Filter Using Active Disturbance Rejection Control

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Nixuan Liu ◽  
Siqi Cao ◽  
Juntao Fei
Keyword(s):  
Fractional Order ◽  
Disturbance Rejection ◽  
Pid Controller ◽  
Control Method ◽  
Active Power Filter ◽  
Power Filter ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

This paper proposed a fractional-order PID controller and active disturbance rejection control (ADRC) method for the current compensation of active power filter (APF). The control method consists of two closed loops. One is a reference current tracking loop based on the ADRC controller, which can treat the internal and external uncertainties of the system as a whole. The other is the voltage control loop with the fractional-order PID controller for more flexibility. Simulation results demonstrate that the proposed control method has a stronger robustness and higher compensating precision comparing with the double-loop PID control method.

A Fractional-Order Active Disturbance Rejection Controller Design for a PMSM Servo System

2021 ◽  
Author(s):  
Bolin Li ◽  
Pengchong Chen ◽  
Ying Luo
Keyword(s):  
Fractional Order ◽  
Disturbance Rejection ◽  
Controller Design ◽  
Tracking Error ◽  
Servo System ◽  
Parameter Tuning ◽  
Phase Constraint ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

Abstract This paper proposes a fractional-order active disturbance rejection controller (FOADRC) parameters tuning design method for a permanent magnet synchronous motor (PMSM) servo system with frequency-domain analysis of active disturbance rejection control. In this method, a fractional-order proportional derivative (FOPD) and a fractional-order [proportional derivative] (FO[PD]) controllers design combining with active disturbance rejection control (ADRC) are proposed. A systematic parameter tuning scheme is presented, and a flat phase constraint is applied as a design specification, which guarantees the phase is flat around the gain crossover frequency point, and the closed-loop system is robust to gain variations. The simulation results illustrate that the response of the PMSM servo system using FO[PD]-ADRC has a smaller overshoot, less tracking error, and better resistance to load disturbance than that using FOPD-ADRC.

scholarly journals A Modified 2-DOF Control Framework and GA Based Intelligent Tuning of PID Controllers

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 423
Author(s):  
Gun-Baek So
Keyword(s):  
Disturbance Rejection ◽  
Pid Controller ◽  
Weighting Function ◽  
Pid Controllers ◽  
Set Point ◽  
Tuning Method ◽  
Point Tracking ◽  
Load Disturbance ◽  
Control Framework ◽  
Feedforward Controller

Although a controller is well-tuned for set-point tracking, it shows poor control results for load disturbance rejection and vice versa. In this paper, a modified two-degree-of-freedom (2-DOF) control framework to solve this problem is proposed, and an optimal tuning method for the pa-rameters of each proportional integral derivative (PID) controller is discussed. The unique feature of the proposed scheme is that a feedforward controller is embedded in the parallel control structure to improve set-point tracking performance. This feedforward controller and the standard PID con-troller are combined to create a new set-point weighted PID controller with a set-point weighting function. Therefore, in this study, two controllers are used: a set-point weighted PID controller for set-point tracking and a conventional PID controller for load disturbance rejection. The parameters included in the two controllers are tuned separately to improve set-point tracking and load dis-turbance rejection performances, respectively. Each controller is optimally tuned by genetic algo-rithm (GA) in terms of minimizing the IAE performance index, and what is special at this time is that it also tunes the set-point weighting parameter simultaneously. The simulation results performed on four virtual processes verify that the proposed method shows better performance in set-point tracking and load disturbance rejection than those of the other methods.

Stability control for end effect of mobile manipulator in uneven terrain based on active disturbance rejection control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chuang Cheng ◽  
Hui Zhang ◽  
Hui Peng ◽  
Zhiqian Zhou ◽  
Bailiang Chen ◽  
...  
Keyword(s):  
Disturbance Rejection ◽  
Control Method ◽  
External Disturbance ◽  
Stability Control ◽  
Mobile Manipulator ◽  
Active Disturbance Rejection Control ◽  
End Effector ◽  
Content Type ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

Purpose When the mobile manipulator is traveling on an unconstructed terrain, the external disturbance is generated. The load on the end of the mobile manipulator will be affected strictly by the disturbance. The purpose of this paper is to reject the disturbance and keep the end effector in a stable pose all the time, a control method is proposed for the onboard manipulator. Design/methodology/approach In this paper, the kinematics and dynamics models of the end pose stability control system for the tracked robot are built. Through the guidance of this model information, the control framework based on active disturbance rejection control (ADRC) is designed, which keeps the attitude of the end of the manipulator stable in the pitch, roll and yaw direction. Meanwhile, the control algorithm is operated with cloud computing because the research object, the rescue robot, aims to be lightweight and execute work with remote manipulation. Findings The challenging simulation experiments demonstrate that the methodology can achieve valid stability control performance in the challenging terrain road in terms of robustness and real-time. Originality/value This research facilitates the stable posture control of the end-effector of the mobile manipulator and maintains it in a suitable stable operating environment. The entire system can normally work even in dynamic disturbance scenarios and uncertain nonlinear modeling. Furthermore, an example is given to guide the parameter tuning of ADRC by using model information and estimate the unknown internal modeling uncertainty, which is difficult to be modeled or identified.

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