Interval type-2 fuzzy neural network-based adaptive compensation control for omni-directional mobile robot

In order to overcome the influence of model uncertainty and external disturbance on the trajectory tracking accuracy of four-wheel omnidirectional mobile robot (FM-OMR), a new adaptive trajectory tracking control scheme based on interval type 2 fuzzy neural network approximator (IT2FNNA) is proposed...

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Vydané v:Neural computing & applications Ročník 35; číslo 16; s. 11653 - 11667
Hlavní autori: Qin, Peng, Zhao, Tao, Dian, Songyi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.06.2023
Springer Nature B.V
Predmet:
Accuracy
Adaptive control
Approximation
Artificial Intelligence
Artificial neural networks
Bias
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Kinematics
Mathematical analysis
Neural networks
Original Article
Probability and Statistics in Computer Science
Robot control
Robot dynamics
Robots
Tracking control
Trajectory control
Velocity
Wheels
Interval type-2 fuzzy neural network
Trajectory tracking
Omnidirectional mobile robot
Fuzzy adaptive compensation
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí:In order to overcome the influence of model uncertainty and external disturbance on the trajectory tracking accuracy of four-wheel omnidirectional mobile robot (FM-OMR), a new adaptive trajectory tracking control scheme based on interval type 2 fuzzy neural network approximator (IT2FNNA) is proposed in this paper. Based on the kinematics and dynamics model of FM-OMR, a dual loop trajectory controller is constructed. To improve the adaptive ability of IT2FNNA, an adaptive adjustment method for the network output is proposed. In addition, to improve the approximation accuracy of the IT2FNNA, a bias strategy is proposed based on the FM-OMR model. Finally, the circle track and ”8” track are simulated and tested. The simulation and experimental results show that the bias strategy and adaptive factor really improve the approximation ability and accuracy of IT2FNNA, and the proposed adaptive control scheme has smaller fluctuations, higher accuracy and better steady-state performance.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08309-2