Model Predictive Control System Based on Ladder-Structured GOBF Models

In this work, we address the problem of identification and control of dynamical systems where a generalized orthonormal basis functions (GOBF) model with ladder-structure is used to represent the system. In the identification process, we took a genetic algorithm to optimize the number of functions and the model poles. The identified model is then used as the basis for the implementation of a predictive controller, which incorporates the advantages of this type of modeling in the lack of output feedback. A magnetic levitation system was identified and a model predictive controller was used to stabilize the system. Results show the feasibility of this technique in the control of a real system.

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