Indirect adaptive predictive control applied to an industrial tank level plant - Iván García Martínez (2011)
Predictive control algorithms have found growing acceptance in academic and industrial applications. However, as every control methodology, it has drawbacks, which is the need for an appropriate process model to be available. Therefore, the controller may become limited in applications with nonlinear and time-varying dynamics. When the predictions are not satisfactory due to an inappropriate model, it would be convenient to have an adaptive mechanism to match the process model and the process dynamics. In this work, SISO and MIMO formulations of indirect adaptive predictive control are implemented online to control an industrial tank level plant where process and heating tanks are operated. The experiments were carried out in conditions that are typical of an industrial situation, where the plant is nonlinear, multivariable and time varying. Results from the simulations and from the online implementation showed that the predictive control algorithm is limited when its predictive model is not representative and by implementing an adaptive mechanism these limitations have been overcome. Therefore, results show that the adaptive predictive controller outperforms the predictive controller when operating the industrial tank level plant, highlighting its potential as a solution for industrial control problems.