Data-Driven Active Disturbance Rejection Control of Pendulum Cart Systems

2018 ◽  
Author(s):  
Raul-Cristian Roman ◽  
Mircea-Bogdan Radac ◽  
Cornel Tureac ◽  
Radu-Emil Precup
Keyword(s):  
Disturbance Rejection ◽  
Data Driven ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

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.

scholarly journals Maximum Sensitivity-Constrained Data-Driven Active Disturbance Rejection Control with Application to Airflow Control in Power Plant

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 231 ◽  
Author(s):  
Ting He ◽  
Zhenlong Wu ◽  
Rongqi Shi ◽  
Donghai Li ◽  
Li Sun ◽  
...  
Keyword(s):  
Power Plant ◽  
Disturbance Rejection ◽  
Maximum Sensitivity ◽  
Data Driven ◽  
Tuning Parameter ◽  
Energy Structure ◽  
Active Disturbance Rejection Control ◽  
Uncertain Dynamics ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

The increasing energy demand and the changing of energy structure have imposed higher requirements on the conventional large-scale power plants control. Complexity of the power plant processes and the frequent change of operation condition make the accurate physical models hard to obtain for control design. To this end, a data-driven control strategy, the active disturbance rejection control (ADRC) has received much attention for the estimation and mitigation of uncertain dynamics beyond the canonical form of cascaded integrators. However, the robustness of ADRC is seldom discussed in a quantitative manner. In this study, the maximum sensitivity is used to evaluate and then constrain the robustness of ADRC applied to high-order processes. Firstly, by using the new idea of the vertical asymptote of the Nyquist curve, a preliminary one-parameter-tuning method is developed. Secondly, a quantitative relationship between the maximum sensitivity and the tuning parameter is established using optimization methods. Then, the feasibility and effectiveness of the proposed method is initially verified in the total air flow control of a power plant simulator. Finally, field tests on the secondary airflow control in a 330 MWe circulating fluidized bed confirm the merit of the proposed maximum sensitivity-constrained ADRC tuning.

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.

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