Online tuning of generalized predictive controllers using fuzzy logic

The generalized predictive controller (GPC) is an efficient strategy for controlling processes with time-varying parameters, as long as the GPC tuning parameters are chosen correctly. This study aims to present a new online tuning algorithm for the parameters of the GPC. The controllers are initially tuned by a model simulation (offline), via genetic algorithm, seeking quick answers and a small error. After variations in the setpoint, injection of disturbances in the output of the plant, and variations in the gains of the system operating in closed loop, the algorithm performs an online adjustment of these parameters using Fuzzy Logic. Based on the error information between the setpoint and the controlled variable and the variation of this error, the algorithm readjusts the tuning parameters of the GPC, so the performance of the control system response is not degraded. The algorithm is validated via model simulations representing the main characteristics of industrial plants. In the simulations, tests are presented by applying disturbances in the output of the plant, changing the dynamics of the model, and changing the setpoint. It is shown that the performance indexes of each plant are presented as being at least similar to those presented in [1], because it is still widely used in recent applications, and in some cases of variation of the dynamics of the plant, the proposed algorithm remained with a satisfactory result, while the presented by [1] became unstable.