Trajectory Tracking Control Method for Omnidirectional Mobile Robot Based on Self-Organizing Fuzzy Neural Network and Preview Strategy

This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In pa...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 25; číslo 2; s. 248
Hlavní autoři: Zhao, Tao, Qin, Peng, Zhong, Yuzhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 30.01.2023
MDPI
Témata:
Access control
Algorithms
Approximation
Artificial neural networks
Control
Control methods
Curves
Equipment and supplies
Fuzzy algorithms
Fuzzy logic
Fuzzy sets
Fuzzy systems
Kinematics
Mobile robots
Neural networks
omnidirectional mobile robot
Optimization
preview strategy
Redundancy
Robots
Robust control
self-organizing
Self-organizing systems
Strategy
Tracking control
Tracking systems
Trajectory control
Trajectory planning
trajectory tracking
Uncertainty
Velocity
China
self-organizing
omnidirectional mobile robot
preview strategy
trajectory tracking
ISSN:1099-4300, 1099-4300
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Shrnutí:This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In particular, since the structure of traditional approximation network is preset, it will cause problems such as input constraints and rule redundancy, resulting in low adaptability of the controller. Therefore, a self-organizing algorithm including rule growth and local access is designed according to the tracking control requirements of omnidirectional mobile robots. In addition, a preview strategy (PS) based on Bezier curve trajectory re-planning is proposed to solve the problem of tracking curve instability caused by the lag of tracking starting point. Finally, the simulation verifies the effectiveness of this method in tracking and trajectory starting point optimization.
Bibliografie:ObjectType-Article-1
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ISSN:1099-4300
1099-4300
DOI:10.3390/e25020248