Real-time implementation of an adaptive feedback and feedforward Generalized Predictive Controller

This paper is devoted to the measurable disturbance rejection problem in Generalized Predictive Control (GPC) using an adaptive GPC controller, which combines the process of online system identification and the process of generalized predictive feedback controller design. In this work, the GPC algorithm is extended to the case when the measurable disturbances are available for feedforward control. The proposed adaptive GPC algorithm with/without future disturbance compensation is implemented in real time and experimentally demonstrated for the application of motor revolutions tracking when subjected to deterministic and pseudo-random disturbance signals. The constraint handling capabilities of the controller are considered as well.