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. 2022 Feb 9:2022:4347772.
doi: 10.1155/2022/4347772. eCollection 2022.

Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence

Jie Hu  1
Affiliations

Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence

Jie Hu. Comput Intell Neurosci. .

Abstract

An intelligent controller based on a self-learning interval type-II fuzzy neural network is proposed to make the motion controller of the industrial intelligent robot with good adaptability. This controller has a parallel structure and contains an interval type-II fuzzy neural network and a conventional PD controller. For the design of the interval type-II fuzzy neural network, the interval type-II fuzzy set is established using the slave design method. In the design process of the interval type-II fuzzy set of the front piece, a dual sequence symmetric trapezoidal subordinate function arrangement method is proposed, which makes the self-learning law and stability analysis of the system in an analytic form and facilitates the implementation of the algorithm in hardware. In the design of the neural network self-learning law, a parametric self-learning algorithm based on sliding mode control theory is established to adjust the structural parameters of the interval type-II fuzzy neural network online, and the stability of the system is proved by using Lyapunov's stability theorem. Three sets of validation simulation experiments are given in conjunction with the trajectory tracking problem of the Delta parallel robot. The simulation results show that, in the presence of system uncertainty, the intelligent controller based on interval self-learning interval type-II fuzzy neural network can significantly improve the trajectory tracking accuracy and robustness of the system and make the control system highly adaptable to the environment. Experiments of intelligent control system based on self-learning interval type-II fuzzy neural network and experiments of reusable particle swarm optimal motion planning method are designed, and the effectiveness of the intelligent control system and motion planning method is verified on the experimental platform. The experimental results show that the intelligent control system based on the self-learning interval type-II fuzzy neural network can effectively improve the accuracy and stability of robot trajectory tracking control, and the reusable particle swarm optimal motion planning method can quickly solve the robot motion planning problem with complex constraints online.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Hardware structure of the robot.
Figure 2
Figure 2
Hierarchical circuit diagram of the robot system.
Figure 3
Figure 3
Fuzzy logic control system for Delta's robot.
Figure 4
Figure 4
Trapezoidal affiliation function fuzzification method.
Figure 5
Figure 5
TCP position error using a controller.
Figure 6
Figure 6
Control signals.
Figure 7
Figure 7
Clustering center of the first feature layer of EFPFDB.
Figure 8
Figure 8
Average of the adaptation of RPSOMP and EFPF-PSO.
See this image and copyright information in PMC

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