Adaptive Fuzzy Model Predictive Control for non-minimum phase and uncertain dynamical nonlinear systems
|
| Title | Adaptive Fuzzy Model Predictive Control for non-minimum phase and uncertain dynamical nonlinear systems |
| Authors | |
| Abstract | this paper introduces a method to design a robust adaptive predictive control based on Fuzzy model. The plant to be used as predictive model is simulated by Takagi-Sugeno Fuzzy Model, and the optimization problem is solved by a Genetic Algorithms or Branch and Bound. The method to tune parameters of the model predictive controller based on Lyapunov stability theorem is presented in this paper to bring higher control performance and guaranty Global Asymptotical Stable (GAS) for the closed-loop system. This method is used for nonlinear systems with non-minimum phase (CSTR), uncertain dynamical systems and nonlinear DC motor. The simulation results for the Continuous Stirrer Tank Reactor (CSTR), nonlinear uncertain dynamical system and nolinear DC motor are used for verifying the proposal method. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2012-04-01 |
| Source | Journal of Computers Vol 7, No 4 (2012) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |