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 Speed sensorless model predictive control method for a direct-drive wind energy conversion system

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1394-1402 ◽  
Author(s):  
Shengquan Li ◽  
Juan Li ◽  
Hanwen Wu ◽  
Zhongwen Lin
Keyword(s):  
Model Predictive Control ◽  
Wind Energy ◽  
Predictive Control ◽  
Control Method ◽  
Synchronous Generator ◽  
State Observer ◽  
Extended State Observer ◽  
Direct Drive ◽  
Extended State ◽  
Speed Sensorless

Considering the problems of the internal and external disturbances of wind speed in the direct-drive wind energy conversion system based on a permanent magnet synchronous generator, a novel model predictive control based on the extended state observer method without the accurate mathematical system model is proposed in this paper. First, a model predictive control method is employed as the feedback controller, while the mathematical model of the control system can be adjusted online via the rolling optimization strategy. Second, an extended state observer is introduced to estimate the state variables and lumped disturbances, that is, the internal disturbances including nonlinear characteristic, multi-variety coupling effect, uncertainties of system parameters, and external disturbances including variations of wind speeds and uncertainties of the natural environment. Third, the effect of lumped disturbances can be attenuated by the estimated disturbance value via a feedforward channel. In addition, in order to achieve the real-time speed control performance of the permanent magnet synchronous generator, a speed sensorless algorithm based on a flux observer is proposed to solve the problem of unsuitability of mechanical speed sensor. Finally, the simulation results with several wind speed types show that the proposed sensorless model predictive control with the extended state observer strategy is an effective way to improve the performance of anti-disturbance and ability of tracking maximum wind energy of the wind power 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 Decoupling Attitude Control of a Hypersonic Glide Vehicle Based on a Nonlinear Extended State Observer

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Chen ◽  
Nannan Du ◽  
Yu Han
Keyword(s):  
Attitude Control ◽  
Closed Loop ◽  
Control Method ◽  
Tracking Error ◽  
State Observer ◽  
Extended State Observer ◽  
Decoupling Control ◽  
Extended State ◽  
Loop Control ◽  
Nonlinear Extended State Observer

Aiming at solving the attitude control problem of a hypersonic glide vehicle, this paper proposes a decoupling control method based on a nonlinear extended state observer (NESO). According to the decentralized robust control theory of Tornambè, the coupling terms and the uncertainties are regarded as generalized uncertainties, and the NESO-based estimation and compensation signals are added to the closed-loop control law. The theoretical deduction proves that the proposed method can ensure that the tracking error of the closed-loop system is uniformly bounded. The simulation is carried out on the hypersonic glide vehicle model and compared with the traditional subchannel feedback control method. The simulation results show that the designed decoupling control method has superior control performances, and the influence of channel-coupling and uncertainty is compensated to a great extent.

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