Application of an Autocovariance Least - Squares Method for Model Predictive Control of Hybrid Ventilation in Livestock Stables

In this paper, the implementation of a new autocovariance least-square (ALS) technique for livestock hybrid ventilation systems and associated indoor climate with a model predictive control (MPC) strategy is presented. The design is based on thermal comfort parameters for poultry in barns and a combined dynamic model describing the entire system knowledge. Reference offset-free tracking is achieved using target calculation and quadratic programming and adding a disturbance model that accommodates unmeasured disturbances entering through the process input. The unknown noise covariances are diagnosed and corrected by applying the ALS estimator with the closed loop process data. The comparative simulations show the performance improvement with the ALS estimator in the presence of disturbances and moderate amount of error in the model parameters. The results demonstrate the high potential of ALS methods in improving the best practice of process control and estimation.