Nonlinear adaptive control of heat transfer fluid temperature in a parabolic trough solar power plant

Abstract

Control of highly nonlinear processes such as solar collector fields is usually a challenging task. A common approach to this problem involves deploying a set of operation point range-specific controllers, whose actions are to be combined in a switching strategy. Discontinuities in control actions upon switching may lead to instabilities and, therefore, achieving bumpless transitions is always a concern. In addition, linear adaptive predictive controllers need to cope with nonlinearities by using high adaptation speeds, often leading to model vulnerability in the presence of aggressive perturbations. Finally, most of the proposed solutions rely on complex plant model developments. In this work, a multivariable nonlinear model-based adaptive predictive controller has been developed and tested against a parabolic trough solar power plant simulation. Since the model employed by this controller accounts for process nonlinearities, adaptation speed can be dramatically reduced, therefore increasing model robustness. The controller is easily initialized and is able to identify and track the process dynamics, including its nonlinearities as it evolves with time, thus requiring neither process up-front modeling nor switching. The presented controller outperforms its linear counterpart both in terms of accuracy and robustness and, due to the generality of its design, it is expected to be applicable to a wide class of linear and nonlinear processes.