Reduced‐order extended state observer based event‐triggered sliding mode control for DC‐DC buck converter system With parameter perturbation

2020 ◽  
Author(s):  
Junxiao Wang ◽  
Jiayi Rong ◽  
Li Yu
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Buck Converter ◽  
Extended State Observer ◽  
Extended State ◽  
Parameter Perturbation ◽  
Reduced Order ◽  
Mode Control ◽  
Event Triggered

scholarly journals An extended state observer-based full-order sliding mode control for robotic joint actuated by antagonistic pneumatic artificial muscles

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098603
Author(s):  
Daoxiong Gong ◽  
Mengyao Pei ◽  
Rui He ◽  
Jianjun Yu
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Extended State Observer ◽  
Artificial Muscles ◽  
Extended State ◽  
Robot System ◽  
Mode Control ◽  
Physical Experiments ◽  
Pneumatic Artificial Muscles

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.

Series sliding mode control for gun control system based on extended state observer

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
Keyword(s):  
Control System ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Gun Control ◽  
Extended State Observer ◽  
Extended State ◽  
Mode Control

scholarly journals Echo State Network for Extended State Observer and Sliding Mode Control of Vehicle Drive Motor with Unknown Hysteresis Nonlinearity

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xuehui Gao ◽  
Bo Sun ◽  
Xinyan Hu ◽  
Kun Zhu
Keyword(s):  
Sliding Mode Control ◽  
Electric Vehicle ◽  
Sliding Mode ◽  
State Observer ◽  
Hysteresis Loss ◽  
Extended State Observer ◽  
Hyperbolic Function ◽  
Echo State Network ◽  
Extended State ◽  
Mode Control

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.

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