Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradient Reinforcement Learning
Abstract
This paper uses value gradient learning (VGL) to track a reference trajectory under uncertainties, by computing the optimal left and right torque values for a nonholonomic mobile robot. VGL is a high-performance algorithm in adaptive dynamic programming (ADP). Here, it is used as a critic function after fitting a first-order Sugeno fuzzy neural network (FNN) structure to critic and actor networks. Moreover, this work handles the impacts of unmodeled bounded disturbances with various friction values. The simulation is introduced to compare two approaches. The first uses an actor network that confirms the ability of the mobile robot dynamic model to follow a desired trajectory. This approach demonstrates a significant enhancement of the robot's capability to absorb unstructured disturbance signals and friction effects. The second type of results use a critic-optimal-control approach, calculating the optimal control signal for the affine dynamic model of the robot. This completely removes the actor network to exploit reduced computational complexity with faster responses. The simulation is introduced to compare both cases.
Recommended Citation
S. Al-Dabooni and D. C. Wunsch, "Mobile Robot Control Based on Hybrid Neuro-Fuzzy Value Gradient Reinforcement Learning," Proceedings of the 2017 International Joint Conference on Neural Networks (2017, Anchorage, AK), pp. 2820 - 2827, Institute of Electrical and Electronics Engineers (IEEE), May 2017.
The definitive version is available at https://doi.org/10.1109/IJCNN.2017.7966204
Meeting Name
2017 International Joint Conference on Neural Networks, IJCNN (2017: May 14-19, Anchorage, AK)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Dynamic models; Friction; Fuzzy inference; Fuzzy logic; Fuzzy neural networks; Mobile robots; Reinforcement learning; Robot programming; Robots; Adaptive dynamic programming; Bounded disturbances; High performance algorithms; Mobile robot dynamics; Non-holonomic mobile robots; Nonholonomic dynamics; Reference trajectories; Value-gradient learning; Dynamic programming; Nonholonomic dynamic mobile robot
International Standard Book Number (ISBN)
978-1-5090-6182-2
International Standard Serial Number (ISSN)
2161-4407
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2017
