Adaptive disturbance rejection model predictive control and its application in a selective catalytic reduction denitrification system

2020 ◽  
Vol 140 ◽  
pp. 106963 ◽  
Author(s):  
Lingchao Zeng ◽  
Yiguo Li ◽  
Peizhi Liao ◽  
Shangshang Wei
Keyword(s):  
Model Predictive Control ◽  
Selective Catalytic Reduction ◽  
Predictive Control ◽  
Disturbance Rejection ◽  
Catalytic Reduction

Characteristics-based model predictive control of selective catalytic reduction in diesel-powered vehicles

2016 ◽  
Vol 47 ◽  
pp. 98-110 ◽  
Author(s):  
H. Pakravesh ◽  
I. Aksikas ◽  
M. Votsmeier ◽  
S. Dubljevic ◽  
R.E. Hayes ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Selective Catalytic Reduction ◽  
Predictive Control ◽  
Catalytic Reduction

Diesel Engine Selective Catalytic Reduction (SCR) Ammonia Surface Coverage Control Using a Computationally-Efficient Model Predictive Control Assisted Method

2009 ◽  
Author(s):  
Ming Feng Hsieh ◽  
Junmin Wang
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Selective Catalytic Reduction ◽  
Predictive Control ◽  
Surface Coverage ◽  
Catalytic Reduction ◽  
Computationally Efficient ◽  
Coverage Ratio ◽  
Control Objective ◽  
Control Authority

This paper presents a diesel engine selective catalytic reduction (SCR) control design based on a novel model predictive control (MPC)-assisted approach, which utilizes the advantages of MPC while keeping the computation demand under an acceptable level. The SCR control problem is featured by the challenges of time delay, significant time-varying characteristics, and limited control authority. Based on the understanding of the SCR reactions, the NH3 surface coverage ratio was selected as the control objective. The proposed MPC-assisted method was compared with conventional controllers such as PID and linear MPC (LMPC). Simulation results exhibited that the MPC-assisted approach can achieve a SCR ammonia surface coverage ratio control with much smaller root mean square error compared to these of other controllers while maintaining a manageable computational demand, and in turn better control of tailpipe NOx and ammonia emissions.

scholarly journals A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 760
Author(s):  
Fang Liu ◽  
Haotian Li ◽  
Ling Liu ◽  
Runmin Zou ◽  
Kangzhi Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Duty Cycle ◽  
Control Strategy ◽  
Disturbance Rejection ◽  
Torque Ripple ◽  
Current Control ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

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