scholarly journals A Theoretical and Experimental Approach of Fuzzy Adaptive Motion Control for Wheeled Autonomous Nonholonomic Vehicles

ISRN Robotics ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Maurizio Melluso
Keyword(s):  
Motion Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Experimental Studies ◽  
Fuzzy Rules ◽  
Tracking Errors ◽  
Inference Mechanism ◽  
Nonholonomic Vehicles ◽  
Fuzzy Inference Mechanism ◽  
Fuzzy Adaptive

A new fuzzy adaptive control is applied to solve a problem of motion control of nonholonomic vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov stability can be ensured. In particular the parameters of the kinematical control law are obtained using a fuzzy mechanism, where the properties of the fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e., fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating ensures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, dynamical and kinematical adaptive controllers have been added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore, system robustness and stability performance are verified through simulations and experimental studies.

Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

Analysis of Pedagogy of Teacher's Capability to Transform Knowledge Into Practice Using Fuzzy Logic

2019 ◽  
Vol 8 (2) ◽  
pp. 16-33
Author(s):  
Jagmohan Mago ◽  
Dinesh Kumar
Keyword(s):  
Fuzzy Logic ◽  
Current Literature ◽  
Fuzzy Inference ◽  
Inference Mechanism ◽  
Common Practices ◽  
Consistent Method ◽  
Fuzzy Inference Mechanism ◽  
Personal Behavior

Current literature and common practices suggest that there is no consistent method available to analyze the performance of teachers. Due to its inherent vagueness and uncertainty, this article analyzes the effectiveness of a teacher depending upon various factors using fuzzy logic. It explains various parameters influencing professional, interpersonal and personal behavior of teachers. Secondly, a fuzzy inference mechanism is developed to decide the possible quality of teachers. The article concludes by observing that the proposed fuzzy logic based system is consistent with that judged by the experts and can be used to predict the possible quality of teachers.

An Innovative Loss-of-Excitation Protection Based on the Fuzzy Inference Mechanism

2010 ◽  
Vol 25 (4) ◽  
pp. 2197-2204 ◽  
Author(s):  
Adriano P. de Morais ◽  
Ghendy Cardoso ◽  
L. Mariotto
Keyword(s):  
Fuzzy Inference ◽  
Inference Mechanism ◽  
Fuzzy Inference Mechanism
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