Abstract
This paper presents a self-tuning PI controller to control the temperature of a greenhouse using natural ventilation. The PI controller parameters are adapted according to the changing dynamics of the process, identified with a simplified greenhouse temperature model based on first principles. The time-varying model parameters are estimated online using the random scaling-based bat algorithm. The model is linearized to obtain a first-order transfer function which facilitates the design of the PI controller using well-known tuning methods. Simulated results with real greenhouse data show that the proposed solution could be applied to keep controllers tuned throughout different agri-seasons.
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