Neural-network-state-observation-based adaptive inversion control method of maglev train

2022 ◽  
pp. 1-1
Author(s):  
Chen Chen ◽  
Junqi Xu ◽  
Lijun Rong ◽  
Wen Ji ◽  
Guobin Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Maglev Train ◽  
State Observation ◽  
Network State

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

scholarly journals Formation Control of Unmanned Vessels with Saturation Constraint and Extended State Observation

2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Huixuan Fu ◽  
Shichuan Wang ◽  
Yan Ji ◽  
Yuchao Wang
Keyword(s):  
Formation Control ◽  
Control Method ◽  
Tracking Error ◽  
External Environment ◽  
Control Force ◽  
Extended State ◽  
Parameter Perturbation ◽  
State Observation ◽  
Saturation Constraint ◽  
The Stability

This paper addressed the formation control problem of surface unmanned vessels with model uncertainty, parameter perturbation, and unknown environmental disturbances. A formation control method based on the control force saturation constraint and the extended state observer (ESO) was proposed. Compared with the control methods which only consider the disturbances from external environment, the method proposed in this paper took model uncertainties, parameter perturbation, and external environment disturbances as the compound disturbances, and the ESO was used to estimate and compensate for the disturbances, which improved the anti-disturbance performance of the controller. The formation controller was designed with the virtual leader strategy, and backstepping technique was designed with saturation constraint (SC) function to avoid the lack of force of the actuator. The stability of the closed-loop system was analyzed with the Lyapunov method, and it was proved that the whole system is uniformly and ultimately bounded. The tracking error can converge to arbitrarily small by choosing reasonable controller parameters. The comparison and analysis of simulation experiments showed that the controller designed in this paper had strong anti-disturbance and anti-saturation performance to the compound disturbances of vessels and can effectively complete the formation control.

Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Equivalent Model ◽  
Gradient Descent Method ◽  
Nonlinear Simulation ◽  
Control Approach ◽  
Active Noise ◽  
Nonlinear Noise

The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.

Trajectory Control Method Of UAV In Autonomous Flight Mode Using Neural Network MELM Algorithm

2020 ◽  
Author(s):  
Oleksii Fesenko ◽  
Robert Bieliakov ◽  
Hrygorii Radzivilov ◽  
Volodymyr Hulii ◽  
Oleh Kovalchuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Trajectory Control ◽  
Autonomous Flight ◽  
Flight Mode

scholarly journals Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

2021 ◽  
Vol 12 (1) ◽  
pp. 69-83
Author(s):  
Saygin Siddiq Ahmed ◽  
Ahmed R. J. Almusawi ◽  
Bülent Yilmaz ◽  
Nuran Dogru
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Low Cost ◽  
Hand Movement ◽  
Cost Effective ◽  
Network Control ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Myoelectric Prosthesis ◽  
System File

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.

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