scholarly journals Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1548 ◽  
Author(s):  
Raul-Cristian Roman ◽  
Radu-Emil Precup ◽  
Emil M. Petriu ◽  
Florin Dragan
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Disturbance Rejection ◽  
Second Order ◽  
Data Driven ◽  
Active Disturbance Rejection Control ◽  
Tower Crane ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Takagi Sugeno

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

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.

A Self-Repairing Control Scheme for Quadrotor Helicopter via Active Disturbance Rejection Control

2013 ◽  
Vol 404 ◽  
pp. 603-608
Author(s):  
Qing Bo Wu ◽  
Fu Yang Chen ◽  
Chang Yun Wen
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Disturbance Rejection ◽  
Control Algorithm ◽  
Tracking Error ◽  
Active Disturbance Rejection Control ◽  
Quadrotor Helicopter ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Control Scheme

In this paper, a self-repairing control scheme for attitude control of a quadrotor helicopter via active disturbance rejection control is proposed. Firstly, a model of the quadrotor helicopter is gained by its dynamic equations with pitch, roll and yaw axis. Then the active disturbance rejection controller is introduced, which is used to design the control system. The control system consists of PID controller in inner-loop and ADRC controller in outer-loop. Disturbances and uncertainties can be compensated by the ADRC to achieve smaller tracking error. Finally, the simulation results of the four-rotor helicopter validate the efficiency and self-repairing capability of the proposed control algorithm, compared with that of the PID control and the separate ADRC control.

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