Fuzzy modeling and control of multilayer incinerator
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Deep combination of fuzzy inference and neural network in fuzzy inference software—FINEST
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Optimal design of neural nets using hybrid algorithms
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy based forecasting approach for rush order control applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive neuro fuzzy controller for adaptive compliant robotic gripper
Expert Systems with Applications: An International Journal
Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.