OPTIMAL ADHESION BRAKING CONTROL OF TRAINS BASED ON PARAMETER ESTIMATION AND SLIDING MODE OBSERVER

2020 ◽  
Vol 48 (4) ◽  
Author(s):  
Jing He ◽  
Laicheng Shi ◽  
Changfan Zhang ◽  
Jianhua Liu ◽  
Buchong Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Braking Control

Modeling and online parameter estimation of intake manifold in gasoline engines using sliding mode observer

2013 ◽  
Vol 32 ◽  
pp. 138-154 ◽  
Author(s):  
Qarab Raza Butt ◽  
Aamer Iqbal Bhatti ◽  
Mohammed Rafiq Mufti ◽  
Mudassar Abbas Rizvi ◽  
Irfan Awan
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Intake Manifold ◽  
Gasoline Engines

scholarly journals Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 1040 ◽  
Author(s):  
Xiaodong Chang ◽  
Jinquan Huang ◽  
Feng Lu
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Second Order ◽  
Sliding Mode Observer ◽  
Turbofan Engine ◽  
Second Order Sliding Mode

scholarly journals Sliding Mode Observer with Adaptive Parameter Estimation for Sensorless Control of IPMSM

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5991
Author(s):  
Yubo Liu ◽  
Junlong Fang ◽  
Kezhu Tan ◽  
Boyan Huang ◽  
Wenshuai He
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Sensorless Control ◽  
Parameters Estimation ◽  
Sliding Mode Observer ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Adaptive Parameter ◽  
Low Pass ◽  
Twisting Algorithm

To improve the observation accuracy and robustness of the sensorless control of an interior permanent magnet synchronous motor (IPMSM), a sliding mode observer based on the super twisting algorithm (STA-SMO) with adaptive parameters estimation control is proposed, as parameter mismatches are considered. First, the conventional sliding mode observer (CSMO) is analyzed. The conventional exponential approach law produces a large chattering phenomenon in the back EMF estimation, which causes a large observation error when filtering the chattering through the low-pass filter. Second, a high-order approach law of the super twisting algorithm is introduced to observe the rotor position and speed estimation, which uses the integral function to eliminate the chattering of the sliding mode. Third, an adaptive parameter estimation control (APEC) is presented to enhance the observation accuracy caused by parameter mismatches; the motor parameter adaptive law of the APEC is designed by Lyapunov’s stability law. Finally, the proposed method not only reduces both the chattering and the low-pass filter, but it also enhances accuracy and robustness against parameter mismatches, as discussed through simulations and experiments.

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