A practical NMPC with robustness of stability applied to distributed solar power plants

This paper presents the application of a Nonlinear Model Predictive Controller (NMPC) to a distributed solar collector field. The control technique is basically similar to Dynamic Matrix Control (DMC) but in the proposed approach a nonlinear model of the process is directly used without linearization of the process model involved in the control strategy. Moreover, a modified Practical Nonlinear Model Predictive Controller (PNMPC) algorithm adapted to solar plant is developed in this work. To include robustness of stability against uncertainties in the NMPC algorithm, a candidate Lyapunov function is included in the cost function. The main purpose of the controller is to manipulate the oil flow rate to maintain the field outlet temperature in the desired reference value and attenuate the disturbances effects. The simulated process used is a distributed parameter model, while for the prediction a lumped parameter model with time delay was considered.

► The control problem of a solar power plant with DCS (ACUREX) is discussed.
► A new practical NMPC to regulate the temperature of solar power plants is presented.
► Stability and robustness are guaranteed by a Lyapunov function approach.
► The proposed NMPC algorithm has a low computational cost.
► Case studies show the system performance with different model uncertainties.