It is important to account for the gap between the dynamics of a plant to be controlled and its mathematical model to improve the control performance. Hence, a controller is designed based on this. In this study, the gap is termed as a lumped disturbance, and is considered with respect to the polynomial fuzzy model that is more effective to represent the plant dynamics. More specifically, a design of state observer is first proposed under the existence of the lumped disturbance, and this is then followed by the proposal of a disturbance observer to estimate the lumped disturbance for further use in the controller design. Finally, a controller is developed for the case in which the control state as well as the lumped disturbance are unavailable. Additionally, computer simulations are provided to illustrate the effectiveness of the proposed approach.

State and disturbance observers-based polynomial fuzzy controller

Karimi, Hamid Reza
2017-01-01

Abstract

It is important to account for the gap between the dynamics of a plant to be controlled and its mathematical model to improve the control performance. Hence, a controller is designed based on this. In this study, the gap is termed as a lumped disturbance, and is considered with respect to the polynomial fuzzy model that is more effective to represent the plant dynamics. More specifically, a design of state observer is first proposed under the existence of the lumped disturbance, and this is then followed by the proposal of a disturbance observer to estimate the lumped disturbance for further use in the controller design. Finally, a controller is developed for the case in which the control state as well as the lumped disturbance are unavailable. Additionally, computer simulations are provided to illustrate the effectiveness of the proposed approach.
2017
Disturbance observer; Observer-based controller; Polynomial fuzzy model; SOS; State observer; Software; Control and Systems Engineering; Theoretical Computer Science; Computer Science Applications1707 Computer Vision and Pattern Recognition; Information Systems and Management; Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1063881
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