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. 2022 Jul 22:2022:4461546.
doi: 10.1155/2022/4461546. eCollection 2022.

Vibration and Trajectory Tracking Control of Engineering Mechanical Arm Based on Neural Network

Xinjun Lei  1   2 , Yunxin Wu  1   3   4
Affiliations

Vibration and Trajectory Tracking Control of Engineering Mechanical Arm Based on Neural Network

Xinjun Lei et al. Comput Intell Neurosci. .

Abstract

We offer a neural network-based control method to control the vibration of the engineering mechanical arm and the trajectory in order to solve the problem of large errors in tracking the path when the engineering mechanical arm is unstable and under the influence of the outside world. A mechanical arm network is used to perform tasks related to learning the unknown dynamic properties of a engineering mechanical arms keyboard without the need for prior learning. Given the dynamic equations of the engineering mechanical arm, the dynamic properties of the mechanical arm were studied using a positive feedback network. The adaptive neural network management system was developed, and the stability and integrity of the closed-loop system were proved by Lyapunov's function. Engineering mechanical arm motion trajectory control errors were modeled and validated in the Matlab/Simulink environment. The simulation results show that the management of the adaptive neural network is able to better control the desired path of the engineering mechanical arm in the presence of external interference, and the fluctuation range of input torque is small. The PID control has a large error in the expected trajectory tracking of the engineering mechanical arm, the fluctuation range of the input torque is as high as 20, and the jitter phenomenon is more serious. The use of detailed comparisons and adaptive neural network monitoring can perform well in manipulating the trajectory of the engineering mechanical arm. The engineering mechanical arm uses an adaptive neural network control method, in which the control precision of engineering mechanical arm motion trajectory can be improved and the out-of-control phenomenon of mechanical arm motion can be reduced.

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Conflict of interest statement

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
Feedforward neural network.
Figure 2
Figure 2
General structure of neural network control system R (T).
Figure 3
Figure 3
Control structure of mechanical arm.
Figure 4
Figure 4
Dynamic control structure of mechanical arm.
Figure 5
Figure 5
Schematic diagram of mechanical arm model.
Figure 6
Figure 6
Movement track of engineering mechanical arm 3 (without external interference).
Figure 7
Figure 7
Torque control of engineering mechanical arm 3 (no external interference).
Figure 8
Figure 8
Movement track of engineering mechanical arm 3 (with external interference).
Figure 9
Figure 9
Torque control of engineering mechanical arm 3 (with external interference).
See this image and copyright information in PMC

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