Nonlinear Predictive Control Application to a Solar Thermal Process

Abstract

Solar thermal plants have high nonlinearities and non-manipulated energy source which make their control task a very challenging work. Linear controllers can’t cope with undesirable deviations of the outlet temperature over all the operation range of the dynamics of this type of plants. Moreover, nonlinear predictive control relying on online nonlinear optimisation have the drawback of time consuming and numerical calculus issues. In this work, neural nonlinear predictive control and an infinite gain scheduling neural predictive control are designed and applied to control the temperature in a distributed parabolic trough solar collector field. The performance of both tracking and disturbance rejection of the proposed controller is compared to those the nonlinear predictive control strategies. The superiority of the proposed control strategy is well demonstrated through some indices in simulation results. The thesis concludes with recommendations and perspectives for future works