Abstract
Scaling factor tuning is one of the most used method to enhance the performance of a fuzzy controller. This paper presents two intelligent tuning strategies to tune this factor. In the first strategy, a supervisor fuzzy controller SFC was designed to continuously adjust, on line, the scaling factor of the basic fuzzy controller BFC based on the error and change of error signals. In the second strategy, a neural network NN is used to do this task. Performance of the tuning strategies are compared with corresponding conventional fuzzy controller in terms of several performance measures such as steady state error, settling time, rising time, and peak overshoot. Simulation results show that SFC performance is better. The system implementation and tests are carried out using LabVIEW (V 8.2).
Highlights
Fuzzy logic controllers FLCs have rapidly gained popularity in many engineering disciplines after the concept of fuzzy logic was introduced by Zadeh and after Mamdani and his coworkers presented a fuzzy control scheme [1]
Focused on the tough job of finding proper scaling factors especially output scaling factor in the control process, this paper presents an intelligent tuning strategies for acquiring the right output scaling factor
Due to the fact that tuning the gain of the FLC is difficult to set with iterative manual process, a fuzzy controller with an intelligent tuning strategies of the output scaling factor is proposed in this paper
Summary
Fuzzy logic controllers FLCs have rapidly gained popularity in many engineering disciplines after the concept of fuzzy logic was introduced by Zadeh and after Mamdani and his coworkers presented a fuzzy control scheme [1]. For the successful design of FLC’s proper selection of input and output scaling factors (SF’s) and/or tuning of the other controller parameters are crucial jobs, which in many cases are done through trial and error or based on some training data [3]. The control performance of the FLCs can be enhanced by the following ways: modifying membership functions [4], inference mechanism improving [5], rule tuning [6] and scaling factors adjusting [7] [8]. Suppose that the inductance La=0.1 Henry, the open loop transfer function of the third order model is: 250 A G (s). If La is neglected, the open loop transfer function of the second order model becomes:[10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
