Fractional-order multi-model predictive control for nonlinear processes

Abstract

This paper proposes a novel fractional-order multi-model predictive control (FO-MMPC) design to deal with a class of nonlinear fractional order systems. Based on the assumption that the plant is governed by a fractional order nonlinear dynamical model, we are able for each operating region, to determine the linear portion of the nonlinear process as a single fractional order pole transfer function. The predictive model is then approximated by rational transfer functions using the singularity function approach in the frequency domain. The main contribution in this control approach is the use of a switching algorithm between fractional order prediction models that are approximating the nonlinear system dynamics for different operating points and input range intervals. By using the FO-MMPC for controlling the level of a conical tank system with nonlinear dynamics using a multiple fractional-order predictive models, it is shown by means of numerical simulations the effectiveness of the proposed control strategy.