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.

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

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775248 ◽  
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.

scholarly journals A Novel Design of Interval Type-2 Neuro-Fuzzy Controller for Flexible Structure

Mechanika ◽  
2021 ◽  
Vol 27 (4) ◽  
pp. 301-310
Author(s):  
Mustafa TINKIR ◽  
Mete KALYONCU ◽  
Hasmet SEZGEN
Keyword(s):  
Control Method ◽  
Fuzzy Controller ◽  
Experimental Models ◽  
Flexible Structure ◽  
Northridge Earthquake ◽  
Neuro Fuzzy ◽  
Active Vibration ◽  
Interval Type ◽  
Novel Design

The aim of this research is to develop a novel design of interval type-2 neuro-fuzzy (IT2NF) controller for active vibration control of a flexible structure during an earthquake. For this purpose, two adaptive neural network based fuzzy logic controllers are designed and combined to create the novel design of an IT2NF controller to reduce the vibrations of two-storey flexible building model that occur during earthquake disturbance effects. Accordingly, dynamic modeling of a flexible structure is realized and simulated using the MATLAB / SimMechanics. Then, an experimental setup consisting of two-storey flexible structure, active mass damper (AMD) and shaker is established. Additionally, IT2NF controller is implemented in simulation and experimental models, and the effectiveness and performance of the IT2NF controller are tested under the scaled Northridge Earthquake acceleration. The obtained simulations and experimental responses are evaluated in terms of cart displacements, deflections, and accelerations of the flexible floors showing a good agreement between the simulations and the experimental results. The results show that the designed novel IT2NF controller reduced the total deflections of first and second floor by 72.3% and 68.7%, respectively, when compared with the uncontrolled system. Additionally, it is also found that the designed IT2NF controller is able to reduce the accelerations of the first and second floor by 64.8% and 54.6%, respectively. The proposed and designed control method reported in this study can be employed as an active vibration controller for multi-degree of freedom of flexible systems under the disturbances such as earthquake excitations.

Using a Type-2 Neural Fuzzy Controller for Navigation Control of Evolutionary Robots

2019 ◽  
Vol 31 (9) ◽  
pp. 2735
Author(s):  
Cheng-Jian Lin ◽  
Jyun-Yu Jhang ◽  
Kuu-Young Young
Keyword(s):  
Fuzzy Controller ◽  
Navigation Control ◽  
Neural Fuzzy

scholarly journals Type-2 Combined T-S Adaptive Fuzzy Control

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yunli Hao ◽  
Shuang Li ◽  
Qing Xia ◽  
Maohua Wang
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Synthesis Method ◽  
Approximation Error ◽  
Fuzzy Model ◽  
Input Variables ◽  
Interval Type

For a class of nonlinear systems with a nonlinear relationship between input and output, a fuzzy control method combining interval type-2 and T-S fuzzy controller is proposed based on type-2 fuzzy system theory. In order to ensure its stability, anti-interference ability, and minimum approximation error, this design combines direct, indirect, supervised, and compensation control types to construct the controller. In this way, the structure of the controller not only has the characteristics of the type-2 fuzzy set, which can reduce the uncertainty of rules, but also has a T-S fuzzy model with linear combination of input variables, which can improve the modeling accuracy and reduce the number of rules of the system. By using the Lyapunov synthesis method, the global stability and the convergence of the closed-loop system under the condition that all variables are uniformly bounded are analyzed, and the adaptive laws of the system parameters are given as well. Finally, the effectiveness and superiority of the proposed method are verified by simulation.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

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