Data-driven Correlation Approach Applied to Load Disturbance Rejection in a Thermal Process

2021 ◽  
Author(s):  
Roger W. Pinto da Silva ◽  
Diego Eckhard
Keyword(s):  
Disturbance Rejection ◽  
Thermal Process ◽  
Data Driven ◽  
Load Disturbance

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.

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 Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice

2020 ◽  
Vol 12 (10) ◽  
pp. 4171
Author(s):  
Qianchao Wang ◽  
Hongcan Xu ◽  
Lei Pan ◽  
Li Sun
Keyword(s):  
Disturbance Rejection ◽  
Critical Role ◽  
Back Propagation ◽  
Data Driven ◽  
Active Disturbance Rejection Control ◽  
Time Data ◽  
Plant Safety ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Forced Draft

Boiler forced draft systems play a critical role in maintaining power plant safety and efficiency. However, their control is notoriously intractable in terms of modelling difficulty, multiple disturbances and severe noise. To this end, this paper develops a data-driven paradigm by combining some popular data analytics methods in both modelling and control. First, singular value decomposition (SVD) is utilized for data classification, which further cooperates with back propagation (BP) neural network to de-noise the measurements. Second, prediction error method (PEM) is used to analyze the historical data and identify the dynamic model, whose responses agree well with the actual plant data. Third, by estimating the lumped disturbances via the real-time data, active disturbance rejection control (ADRC) is employed to control the forced draft system, whose stability is analyzed in the frequency domain. Simulation results demonstrate the efficiency and superiority of the proposed method over proportional-integral-differential (PID) controller and model predictive controller, depicting a promising prospect in the future industry practice.

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