Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability

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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EEG signals classifications of motor imagery using adaptive neuro-fuzzy inference system and interval type-2 fuzzy system

International Journal of Intelligent Systems Technologies and Applications ◽

10.1504/ijista.2017.084220 ◽

2017 ◽

Vol 16 (2) ◽

pp. 106

Author(s):

Shereen A. El aal ◽

Rabie A. Ramadan ◽

Neveen I. Ghali

Keyword(s):

Motor Imagery ◽

Fuzzy Inference System ◽

Fuzzy System ◽

Fuzzy Inference ◽

Eeg Signals ◽

Inference System ◽

Neuro Fuzzy ◽

Interval Type

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

2013 CACS International Automatic Control Conference (CACS) ◽

10.1109/cacs.2013.6734165 ◽

2013 ◽

Author(s):

Chaio-Shiung Chen ◽

Yung-Sheng Wang

Keyword(s):

Inverse Control ◽

Interval Type ◽

Fuzzy Network ◽

Neural Fuzzy Network ◽

Neural Fuzzy ◽

Self Adaptive

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Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution

Advances in Mechanical Engineering ◽

10.1177/1687814017752483 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401775248 ◽

Cited By ~ 3

Author(s):

Tzu-Chao Lin ◽

Chao-Chun Chen ◽

Cheng-Jian Lin

Keyword(s):

Mobile Robot ◽

Differential Evolution ◽

Fuzzy Controller ◽

Dynamic Group ◽

Unknown Environments ◽

Navigation Control ◽

Interval Type ◽

Wall Following ◽

Neural Fuzzy

This study developed and effectively implemented an efficient navigation control of a mobile robot in unknown environments. The proposed navigation control method consists of mode manager, wall-following mode, and towards-goal mode. The interval type-2 neural fuzzy controller optimized by the dynamic group differential evolution is exploited for reinforcement learning to develop an adaptive wall-following controller. The wall-following performance of the robot is evaluated by a proposed fitness function. The mode manager switches to the proper mode according to the relation between the mobile robot and the environment, and an escape mechanism is added to prevent the robot falling into the dead cycle. The experimental results of wall-following show that dynamic group differential evolution is superior to other methods. In addition, the navigation control results further show that the moving track of proposed model is better than other methods and it successfully completes the navigation control in unknown environments.

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