scholarly journals Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 797 ◽  
Author(s):  
Yili Gu ◽  
Zhiqiang Li ◽  
Zhen Zhang ◽  
Jun Li ◽  
Liqing Chen
Keyword(s):  
Neural Network ◽  
Control System ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Soft Soil ◽  
Simulation Software ◽  
Dynamics Simulation ◽  
Information Collection ◽  
Soil Conditions ◽  
Field Information

Due to the narrow row spacing of corn, the lack of light in the field caused by the blocking of branches, leaves and weeds in the middle and late stages of corn growth, it is generally difficult for machinery to move between rows and also impossible to observe the corn growth in real time. To solve the problem, a robot for corn interlines information collection thus is designed. First, the mathematical model of the robot is established using the designed control system. Second, an improved convolutional neural network model is proposed for training and learning, and the driving path is fitted by detecting and identifying corn rhizomes. Next, a multi-body dynamics simulation software, RecurDyn/track, is used to establish a dynamic model of the robot movement in soft soil conditions, and a control system is developed in MATLAB/SIMULINK for joint simulation experiments. Simulation results show that the method for controlling a sliding-mode variable structure can achieve better control results. Finally, experiments on the ground and in a simulated field environment show that the robot for field information collection based on the method developed runs stably and shows little deviation. The robot can be well applied for field plant protection, the control of corn diseases and insect pests, and the realization of human–machine separation.

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

2018 ◽  
Vol 41 (5) ◽  
pp. 1383-1394 ◽  
Author(s):  
Xuan Yao ◽  
Zhaobo Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sliding Mode Control ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Trajectory Control ◽  
Active Magnetic Bearing ◽  
Learning Method ◽  
Mode Control

Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

scholarly journals Neural network–based terminal sliding mode applied to position/force adaptive control for constrained robotic manipulators

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878128 ◽  
Author(s):  
Chongzhen Cao ◽  
Fengqin Wang ◽  
Qianlei Cao ◽  
Hui Sun ◽  
Wei Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Position Control ◽  
Simulation Analysis ◽  
Sliding Mode ◽  
Control Strategies ◽  
Robotic Manipulators ◽  
Dynamics Simulation ◽  
Position Tracking ◽  
Terminal Sliding Mode

This study considers the control problem of constrained robotic manipulators with dynamic uncertainties. A new force/position control strategy is proposed based upon terminal sliding mode and neural network. The terminal sliding mode combines position tracking with velocity tracking, and the neural network estimates unknown dynamics. Then, an adaptive control law is utilized to ensure finite-time convergent performance of position tracking and boundedness of contacting force tracking. Compared with existing force/position control strategies, the proposed strategy ensures the convergent performance without nominal model of the system dynamics. Simulation analysis verifies that the proposed strategy is effective.

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