Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
| Hi-index | 0.00 |
In this paper, the level control in coupled tanks is investigated. At first a feedback linearization method is applied. It is a well known fact that feedback linearization controllers are not robust to changes in the parameters of the system and to disturbances acting on the system. Hence a Sliding Mode Controller is designed. In Sliding Mode Control (SMC), fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. Then a Fuzzy Sliding Mode Control (FSMC) in cooperation with Genetic Algorithms (GAs) in coupled tanks problem is studied. A fuzzy logic controller is used to replace the discontinuity in the signoum function of the reaching law in the Sliding-Mode Control (SMC). Parameters of FSMC are adjusted by GAs. Finally, the performance and the significance of the controlled system are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results indicate performance of genetic-based FSMC controller.