Intelligent predictive control of a solar power plant with neuro-fuzzy identifier and evolutionary programming optimizer

The paper presents an intelligent predictive control to govern the dynamics of a solar power plant system. This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neuro-fuzzy predictive control, can be a better match to govern the system dynamics. In our proposed method, a neuro-fuzzy model identifies the future behavior of the systems over a certain prediction horizon while an optimizer algorithm based on EP determines the input sequence. The first value of this sequence is applied to the plant. Using the proposed intelligent predictive controller, the performance of outlet temperature tracking problem in a solar power plant is investigated. Simulation results demonstrate the effectiveness and superiority of the proposed approach.