A Lyapunov theory and stochastic optimization based stable adaptive fuzzy control methodology

The present paper proposes a new methodology for designing stable adaptive fuzzy controllers, where the conventional Lyapunov theory and the particle swarm optimization (PSO) based stochastic approach are clubbed together. The objective is to design a self-adaptive fuzzy controller online, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance. The hybrid controller proposed in this work is implemented for a benchmark case study and the results demonstrate the usefulness of the proposed approach.