Control of nonlinear systems with a linear state-feedback controller and a modified neural network tuned by genetic algorithm

2007 ◽  
Author(s):  
H.K. Lam ◽  
S.H. Ling ◽  
H.H.C. Iu ◽  
C.W. Yeung ◽  
F.H.F. Leung
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
State Feedback ◽  
Feedback Controller ◽  
State Feedback Controller ◽  
Linear State

scholarly journals Proportional-Integral State-Feedback Controller Optimization for a Full-Car Active Suspension Setup using a Genetic Algorithm

2018 ◽  
Vol 51 (4) ◽  
pp. 1-6
Author(s):  
Michiel Haemers ◽  
Stijn Derammelaere ◽  
Clara-Mihaela Ionescu ◽  
Kurt Stockman ◽  
Jasper De Viaene ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
State Feedback ◽  
Active Suspension ◽  
Feedback Controller ◽  
Proportional Integral ◽  
State Feedback Controller

scholarly journals Stabilizing of Subspaces Based on DPGA and Chaos Genetic Algorithm for Optimizing State Feedback Controller

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
M. Hosseinpour ◽  
P. Nikdel ◽  
M. A. Badamchizadeh ◽  
M. A. Poor
Keyword(s):  
Genetic Algorithm ◽  
State Feedback ◽  
Search Space ◽  
Feedback Controller ◽  
State Feedback Controller ◽  
Chaos Algorithm ◽  
Speed Up ◽  
Population Genetic Algorithm ◽  
Domain Of Parameters ◽  
Chaos Genetic Algorithm

The main purpose of the paper is to optimize state feedback parameters using intelligent method, GA, Hermite-Biehler, and chaos algorithm. GA is implemented for local search but it has some deficiencies such as trapping into a local minimum and slow convergence, so the combination of Hermite-Biehler and chaos algorithm has been added to GA to avoid its deficiencies. Dividing search space is usually done by distributed population genetic algorithm (DPGA). Moreover, using generalized Hermite-Biehler Theorem can find the domain of parameters. In order to speed up the convergence at the first step, Hermite-Biehler method finds some intervals for controller, in the next step the GA will be added, and, finally, chaos disturbance will help the algorithm to reach a global minimum. Therefore, the proposed method can optimize the parameters of the state feedback controller.

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