Automatic landing system using brain emotional learning based intelligent controller: A new algorithm

Throughout the history of commercial aviation, the landing phase of the flight has always been a very challenging one for the pilot because of the dangers involved. Hence, designing a controller which has qualified performance during wind gusts and disturbances is so critical, i.e., an automatic landing system should be capable of responding quickly and effectively in a wide range of situations. Due to this issue, the objective of this study is to design a novel intelligent controller inspired by the mammals’ brain to address the landing phase of a commercial aircraft in the presence of disturbances. To highlight the benefits of the proposed method, comparisons are also included between the brain emotional learning based intelligent controller, fuzzy, and Proportional-Integral-Derivative (PID) methods. Through the suitable sensory inputs and reward signals in the algorithms as well as using the learning mechanism, the controller finds the proper control signal to be applied to the actuator, thus this method is able to reject disturbance and eliminate the tracking error without considering the model of the system. The numerical results indicate that using less control effort, the proposed method provides a better solution for the tracking problem in presence of wind gust.