Year: 2017 | Month: December | Volume 4 | Issue 2
Neural Optimized Autotuning Fuzzy Logic Controller for Spherical Tank Process
Boo. Poonguzhali *and R. Vinodha
DOI:10.5958/2454-9533.2017.00005.9
Abstract:
In any real time control application, the controller is expected to give optimal results irrespective of plant non-linearity, operating point change, component non-linearity and saturation. Fuzzy Logic Control (FLC) meets the control objective as it crafts the human experience in the form of If-Then rules. As the gain factors or scaling factors are constant in conventional FLC, Autotuning FLC with tuned scaling factors take over the charge. Further, in real time controllers, the memory constraint is dominant. As a solution, reduction of existing fuzzy rule base by subtractive clustering and optimization of the reduced rule set via neural network is proposed. Autotuning of input scale factors clubbed with reduction and optimization of a controller rule set is proposed to meet out the dynamic control of real time spherical tank process. The efficacy of the proposed approach is compared with real time servo and regulatory results of conventional FLC and AFLC of the spherical tank process
Print This Article Email This Article to Your Friend
