Optimized interval type-2 fuzzy logic controller based on Bio-inspired methods

Recently, interval type-2 fuzzy logic controller (IT2FLC) which is considered a simple class of the general Type-2 fuzzy logic controller (T2FLC) has shown superiority in different applications in dealing with uncertainties and minimizing its impact on the system, in fact superiority of IT2FLC over ordinary Type-1 fuzzy logic controller (T1FLC) in decreasing the effect of uncertainties in the system comes through the best choice of the unique property of IT2FLC called footprint of uncertainty (FOU) which is not included in T1FLC. Varying FOU can save a more degree of freedom in designing IT2FLC. It can be chosen using an appropriate optimization method where the selection of proper optimization method will develop the effectiveness of IT2FLC and increase its ability to minimize uncertainties in the system. Based on the above, an IT2FLC is utilized in this study with its parameters are tuned using different Bio-inspired optimization techniques to determine the effect of tuning FOU that achieves a good system performance. A comparison includes these optimization techniques for IT2FLC such as ant lion optimization (ALO), grey wolf optimization (GWO), artificial hummingbird algorithm (AHA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and T1FLC method is constructed using some performance criteria to determine the proper approach that achieves satisfactory performance. The performance and the effectiveness of the proposed control approach were investigated in Matlab/Simulink platform. Simulation results showed that IT2FLC based on bio-inspired optimization methods always outperforms T1FLC under different scenarios where it shows high stability, and it can dampen out the effect of uncertainties in the system.