scholarly journals Tuning of extended state observer with neural network-based control performance assessment

2022 ◽  
Author(s):  
Piotr Kicki ◽  
Krzysztof Łakomy ◽  
Ki Myung Brian Lee
Keyword(s):  
Neural Network ◽  
Performance Assessment ◽  
State Observer ◽  
Extended State Observer ◽  
Control Performance ◽  
Extended State ◽  
Control Performance Assessment

scholarly journals Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2862 ◽  
Author(s):  
Youjie Ma ◽  
Long Tao ◽  
Xuesong Zhou ◽  
Wei Li ◽  
Xueqi Shi
Keyword(s):  
Fuzzy Logic ◽  
Synchronous Generator ◽  
State Observer ◽  
Fuzzy Logic System ◽  
Extended State Observer ◽  
Control Performance ◽  
Extended State ◽  
Permanent Magnet Synchronous Generator ◽  
Linear Extended State Observer ◽  
Logic System

Recently, wind energy conversion systems (WECSs) have attracted attention due to their effective application in renewable energy sources. It is a complex system with multi-variables, strong coupling, non-linearity, and variable parameters; however, traditional control systems are inadequate in answering the demands of complex systems. In order to solve the complexity and improve the transient stability during grid faults and power fluctuations, this paper proposes a fuzzy logic system with the linear extended state observer (FLS-LESO) applied to WECSs based on a permanent magnet synchronous generator (PMSG). The FLS-LESO consists of a fuzzy logic controller, a conventional PD controller, and the linear extended state observer (LESO). This paper analyzes the mathematical model of a wind power system and combines it with LESO to improve the estimation accuracy of the observer and further improve the control performance. In the simulation study, the control performance of the FLS-LESO was also tested under various operating conditions using the MATLAB/Simulink simulation platform to verify the correctness and effectiveness of the control system.

Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator

2020 ◽  
Vol 234 (7) ◽  
pp. 820-833
Author(s):  
JianTao Yang ◽  
Cheng Peng
Keyword(s):  
Neural Network ◽  
Output Feedback ◽  
Impedance Control ◽  
State Observer ◽  
Lyapunov Theory ◽  
Extended State Observer ◽  
Compact Set ◽  
Extended State ◽  
Adaptive Neural Network ◽  
Tracking Differentiator

Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency.

scholarly journals RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Liu ◽  
Tefang Chen
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Linear Motor ◽  
State Observer ◽  
Network Control ◽  
Extended State Observer ◽  
Direct Drive ◽  
Extended State ◽  
Hydraulic Power ◽  
Loop Control

Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.

scholarly journals Discrete Sliding Mode Control Design for Bilateral Teleoperation System via Adaptive Extended State Observer

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5091
Author(s):  
Yongli Yan ◽  
Li Ding ◽  
Yana Yang ◽  
Fucai Liu
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Extended State Observer ◽  
Control Performance ◽  
Extended State ◽  
Reaching Law ◽  
Mode Control ◽  
Teleoperation System ◽  
Discrete Sliding Mode Control

The goal of this paper is to improve the synchronization control performance of nonlinear teleoperation systems with system uncertainties in the presence of time delays. In view of the nonlinear discrete states of the teleoperation system in packet-switched communication networks, a new discrete sliding mode control (DSMC) strategy is performed via a new reaching law in task space. The new reaching law is designed to reduce the chattering and improve control performance. Moreover, an adaptive extended state observer (AESO) is used to estimate the total system disturbances. The additional gain of AESO is adjusted in time to decrease the estimation errors of both system states and disturbances automatically and improve the estimation performances of the AESO. Finally, the validity of the designed control strategy is demonstrated by both simulation and experiments. Furthermore, the experimental comparison results indicate that the improvement is achievable with the proposed AESO and DSMC.

scholarly journals Fault-tolerant control for a class of n-order systems based on fast terminal sliding mode and extended state observer

2021 ◽  
pp. 002029402110286
Author(s):  
Pu Yang ◽  
Peng Liu ◽  
ChenWan Wen ◽  
Huilin Geng
Keyword(s):  
Fault Tolerant ◽  
Sliding Mode ◽  
Singular Control ◽  
Fault Tolerant Control ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Terminal Sliding Mode ◽  
Fast Terminal Sliding Mode ◽  
Terminal Sliding Surface

This paper focuses on fast terminal sliding mode fault-tolerant control for a class of n-order nonlinear systems. Firstly, when the actuator fault occurs, the extended state observer (ESO) is used to estimate the lumped uncertainty and its derivative of the system, so that the fault boundary is not needed to know. The convergence of ESO is proved theoretically. Secondly, a new type of fast terminal sliding surface is designed to achieve global fast convergence, non-singular control law and chattering reduction, and the Lyapunov stability criterion is used to prove that the system states converge to the origin of the sliding mode surface in finite time, which ensures the stability of the closed-loop system. Finally, the effectiveness and superiority of the proposed algorithm are verified by two simulation experiments of different order systems.

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