Sliding-Neural Mode Controller Optimized by the PSO Algorithm for a Two-Degree Freedom Helicopter System
Abstract
Today, helicopters are widely used in various industries, including aviation and the military. Therefore, control and guidance of this device is of great importance. A 2-degree laboratory freedom helicopter is used to study small-scale helicopters. This device has much simpler dynamics and various experiments can be performed to check and control its condition. Based on these experiments, studies can be performed on more complex large-scale helicopter systems. In this research, while briefly introducing the 2_degree freedom helicopter, an appropriate controller is designed to improve its performance. The designed controller must be able to maintain its stability in tracking the inputs applied to it as a reference input, in the presence of external disturbances such as wind. Also, in case of uncertainty in system parameters such as weight changes, it should remain stable and track the applied inputs well. The controller designed in this study includes sliding mode control in which a neural network is used. In order to improve the results of the particle swarm optimization algorithm is used to determine the slip control parameters.
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