Design of a fractional-order self-tuning regulator using optimization algorithms

The self-tuning regulators form an important sub-class of adaptive controllers. This paper introduces a novel scheme for designing a fractional order self-tuning regulator. Original designs for all the sub-modules of the self-tuning regulator are proposed. The particle swarm optimization algorithm is utilized for online identification of the parameters of the dynamic fractional order process while the subsequent tuning of the controller parameters is performed by differential evolution. Results show that the proposed self-tuning regulator is both precise and robust.