Design of a Supplementary Controller for Power System Stabilizer Using Bacterial Foraging Optimization Algorithm

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

“…Over the years, fuzzy control has evolved as an extremely popular and viable control alternative for the purpose of modeling and control in a variety of applications, ranging from robotics and mechanical systems, to electrical drives, in process control of highly non-linear chemical processes and in other fields of engineering [30][31][32][33][34]. Similarly stochastic optimization techniques, specially biologically-inspired optimization algorithms in particular, have also been employed successfully to solve the adaptive control problems and other related problems such as robotic navigation problems, communication resource allocation problems, power systems control, power electronics and drives related problems [35][36][37][38], etc. In the last decade, such biologically-inspired optimization algorithms and swarm intelligence based algorithms have been successfully employed in conjunction with several direct and indirect adaptive fuzzy control, model predictive control, fuzzy sliding mode control, fuzzy robust control [18,19,29,33,40].…”