Robust inverse control for PMLSM drives using self-adaptive interval type-2 neural fuzzy network

2013 ◽  
Author(s):  
Chaio-Shiung Chen ◽  
Yung-Sheng Wang
Keyword(s):  
Inverse Control ◽  
Interval Type ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy ◽  
Self Adaptive

Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots

2021 ◽  
Author(s):  
Iman Shafieenejad ◽  
Elham Dehghan Rouzi ◽  
Jamshid Sardari ◽  
Mohammad Siami Araghi ◽  
Amirhosein Esmaeili ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Honey Bees ◽  
Bees Algorithm ◽  
Aerial Robots ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

scholarly journals Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4181 ◽  
Author(s):  
Chun-Hui Lin ◽  
Shyh-Hau Wang ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robots ◽  
Control Method ◽  
Fuzzy Controller ◽  
Bee Colony ◽  
Load Carrying ◽  
Navigation Control ◽  
Interval Type ◽  
Navigation Method ◽  
Neural Fuzzy

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

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