Designing a fuzzy logic controller with particle swarm optimisation algorithm

Designing a fuzzy logic controller with particle swarm optimisation algorithm

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Swarm Intelligence - Volume 3: Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, we designed a trajectory-tracking fuzzy logic controller (TTFLC) for the TRIGA Mark-II Training and Research Reactor, which is located at the Istanbul Technical University. The designed fuzzy logic controller (FLC) is based on the zeroorder Sugeno method. The parameters of the FLC membership functions and the action weights of the 15 rules in the rule base are optimised by using the particle swarm optimisation (PSO) algorithm. The objective of this study is to control the TRIGA Mark-II reactor using the designed PSO-tuning TTFLC in a simulator. We used a simulation code from the literature called `YAVCAN' for studying the non-linear behaviour of the core of the TRIGA Mark-II reactor. To select the best parameters of the PSO algorithm for this system, we conducted some experiments. After selecting the best PSO parameters, the algorithm was started a number of times to determine the optimal parameters of the designed FLC and the optimal parameters of the controller. After determining these parameters, the performance of the designed controller was tested for various initial and desired power levels and under conditions of disturbance. The simulation results showed that the proposed controller could control the reactor power successfully, and it could ensure that the reactor tracks the desired trajectory power within the acceptable error tolerance. Therefore, the PSO algorithm is suitable for finding the optimal parameters of the FLC.

Chapter Contents:

  • Abstract
  • 18.1 TRIGA Mark-II reactor
  • 18.1.1 Literature review
  • 18.2 Particle swarm optimisation
  • 18.2.1 Basic PSO algorithm
  • 18.3 Fuzzy logic controller
  • 18.3.1 Structure of FLC
  • 18.4 Realised controller
  • 18.4.1 Trajectory
  • 18.4.2 Designed fuzzy controller (TTFLC)
  • 18.4.3 Coding the controller parameters for the PSO algorithm
  • 18.4.4 Determining limit values of FLC parameters
  • 18.4.5 PSO algorithm parameters
  • Optimisation of fuzzy controller parameters with PSO
  • 18.4.6 PSO–TTFLC simulator's graphical user interface (GUI)
  • 18.5 Controller performance results
  • 18.5.1 Effect of initial power levels
  • 18.5.2 Effect of desired power levels
  • 18.5.3 Effect of period values
  • 18.5.4 Effect of reactivity imports
  • 18.5.5 Effect of transitive trajectory
  • 18.6 Conclusion
  • References

Inspec keywords: fault tolerant control; power system control; fission research reactors; tracking; nonlinear control systems; fuzzy systems; fuzzy control; optimal control; knowledge based systems; power system faults; particle swarm optimisation; trajectory control; reactive power control

Other keywords: FLC membership functions; simulation code; rule base; particle swarm optimisation algorithm; disturbance; zeroorder Sugeno method; YAVCAN; Istanbul technical university; reactor power; nonlinear behaviour; TRIGA mark II training and research reactor; power levels; error tolerance; PSO tuning TTFLC design; trajectory power; trajectory tracking fuzzy logic controller; optimal parameters

Subjects: Nuclear reactors; Fuzzy control; Optimisation techniques; Optimisation techniques; Nonlinear control systems; Control of electric power systems; Spatial variables control; Power system control; Optimal control; Expert systems and other AI software and techniques

Preview this chapter:
Zoom in

Designing a fuzzy logic controller with particle swarm optimisation algorithm, Page 1 of 2

| /docserver/preview/fulltext/books/ce/pbce119h/PBCE119H_ch18-1.gif /docserver/preview/fulltext/books/ce/pbce119h/PBCE119H_ch18-2.gif

Related content

This is a required field
Please enter a valid email address