A Concept for the Dependable Operation of Autonomous Robots by Means of Adaptive Fuzzy Rules

2012 ◽  
Vol 30 (1) ◽  
pp. 17-24
Author(s):  
Karl-Erwin Grosspietsch ◽  
Tanya A. Silaeva
Keyword(s):  
Autonomous Robots ◽  
Fuzzy Rules ◽  
Adaptive Fuzzy

scholarly journals Using Fuzzy Logic in Robot Navigation to Find Animals

2014 ◽  
Vol 12 (4) ◽  
pp. 3382-3392 ◽  
Author(s):  
Mahdi Amiri ◽  
Zeinab Abbasi ◽  
Fakhte Soltani Tafreshi
Keyword(s):  
Fuzzy Logic ◽  
Endangered Species ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
New Method ◽  
Matlab Software ◽  
Rule Based ◽  
Rule Based Systems

 Fuzzy logic is a tool to use human expertise. The simplicity of fuzzy-rule based systems and its power to perform various tasks without accurate measurement and computation makes it very popular between sciences. One of these applications is using fuzzy logic in designing the controller for navigation of autonomous robots to move in various environments. This paper proposes a new method of robot navigation based on fuzzy logic. This method can be drawn upon to design robots which can find and catch different kind of animals, especially endangered species. It works based on a hierarchical set of behavior each of which acts by using a set of fuzzy rules. The proposed method is simulated and tested by MATLAB software.

scholarly journals Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

2019 ◽  
Vol 9 (1) ◽  
pp. 5024-5031
Keyword(s):  
Sliding Mode ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Fuzzy Rules ◽  
System Model ◽  
Fuzzy Membership ◽  
Sliding Mode Controller ◽  
Membership Functions ◽  
Adaptive Fuzzy ◽  
Fuzzy Membership Functions

In the previous decades, the SMC approach has attained unique consideration as this technique offers a systematic model to maintain robust performance and asymptotic stability. As robotic manipulators turn out to be gradually more significant in industrial automation, robotic manipulators by means of SMC have raised as a significant region of research. Hence, this paper intends to model and establish an adaptive sliding mode controller (SMC) for robotic manipulator. As it is not feasible to match up the SMC functions with the system model each time, this paper implements a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. Consequently, it derives the obtained features as fuzzy rules. In the subsequent phase, it signifies the derived fuzzy rules depending on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the external disturbances and noise and validates the effectiveness of the proposed and conventional models.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

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