Smith control of SCR system based on sliding mode control

Author(s):  
Jie Ren ◽  
Cuiping Pu ◽  
An Yu

The reaction process of selective catalytic reduction (SCR) denitrification system is complex, which has the characteristics of large inertia, large delay, strong interference and uncertainty. Traditional PID control can’t achieve accurate control of ammonia injection. Based on linear active disturbance rejection control (LADRC), Smith predictor is used to eliminate the delay output variables before entering extended state observer. The nonlinear state error feedback control law of ADRC structure is designed by using sliding mode control law, which improves the fast response and stability of the system. To solve the problem that the selective catalytic reduction denitrification system is difficult to achieve accurate modeling and large delay, Smith-SMC linear extended state observer (Smith-SMC-LESO) is designed. The simulation results of tracking characteristics, anti-jamming characteristics and robustness show that the set-point tracking performance and anti-jamming ability of Smith-LADRC and Smith-SMC-LESO are significantly improved.

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098603
Author(s):  
Daoxiong Gong ◽  
Mengyao Pei ◽  
Rui He ◽  
Jianjun Yu

Pneumatic artificial muscles (PAMs) are expected to play an important role in endowing the advanced robot with the compliant manipulation, which is very important for a robot to coexist and cooperate with humans. However, the strong nonlinear characteristics of PAMs hinder its wide application in robots, and therefore, advanced control algorithms are urgently needed for making the best use of the advantages and bypassing the disadvantages of PAMs. In this article, we propose a full-order sliding mode control extended state observer (fSMC-ESO) algorithm that combines the ESO and the fSMC for a robotic joint actuated by a pair of antagonistic PAMs. The fSMC is employed to eliminate the chattering and to guarantee the finite-time convergence, and the ESO is adopted to observe both the total disturbance and the states of the robot system, so that we can inhibit the disturbance and compensate the nonlinearity efficiently. Both simulations and physical experiments are conducted to validate the proposed method. We suggest that the proposed method can be applied to the robotic systems actuated by PAMs and remarkably improve the performance of the robot system.


2011 ◽  
Vol 19 (10) ◽  
pp. 2409-2418
Author(s):  
马晓军 MA Xiao-jun ◽  
袁东 YUAN Dong ◽  
李匡成 LI Kuang-cheng ◽  
魏曙光 WEI Shu-guang

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xuehui Gao ◽  
Bo Sun ◽  
Xinyan Hu ◽  
Kun Zhu

An echo state network (ESN) for extended state observer (ESO) and sliding mode control (SMC) of permanent magnet synchronous motor (PMSM) in an electric vehicle system is investigated in this paper. For the PMSM model, most researches neglect the hysteresis loss and other nonlinear factors, which reduces the accuracy of the PMSM model. We present a modified PMSM model considering the hysteresis loss and then transform the new PMSM model to a canonical form to simplify the controller design. In order to deal with the hysteresis loss, an ESN is utilized to estimate the nonlinearity. Considering that some states cannot be directly obtained, an ESO with ESN is proposed to estimate unknown system states of the electric vehicle PMSM system. Afterwards, an SMC is adopted to control the closed-loop system based on the ESO with ESN, and a double hyperbolic function instead of the sign function is used to suppress the chattering of the SMC. The stabilities of the observer and the controller are all guaranteed by Lyapunov functions. Finally, simulations are presented to verify the validity of the echo state network for extended state observer and the neural network sliding mode control.


Sign in / Sign up

Export Citation Format

Share Document