Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Easily Reconfigurable Analytical Fuzzy Predictive Controllers: Actuator Faults Handling
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
International Journal of Applied Mathematics and Computer Science
Fuzzy Modeling and Control
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The easily reconfigurable predictive controllers are supplemented with a mechanism of disturbance measurement utilization. It is done in such a way that the main advantage of the controllers - their simplicity - is maintained. The predictive controllers under consideration are based on fuzzy Takagi-Sugeno (TS) models in which step responses are used as local models. These models are supplemented with the parts describing the influence of disturbances on the outputs of the control plant. Then, the controllers are formulated in such a way that the control signals are easily generated. Efficiency and usefulness of the predictive controllers utilizing disturbance measurement is demonstrated in the example control system of a nonlinear control plant with delay.