Design and Analysis of a Hybrid Intelligent Controller for Longitudinal Dynamics in Autonomous Unmanned Aircraft Systems

Abstract

Given the increasing request for advanced levels of autonomy in Unmanned Aircraft Systems (UAS), the autonomous control of these systems (AUAS) has gained substantial importance and attention between the researchers. The controllers for these systems can be either traditional or intelligent. This research paper highlights on exploring the integration of a smart controller with a proportional integral derivative controller to handle the speed and altitude of an aircraft. This permits the autonomous longitudinal control for unmanned aircraft that includes the takeoff and altitude management. A hybrid control method, which combines numerous control systems, is proposed in this paper. Mainly, it comprises the model predictive control (MPC), neural network control (NNC) and the PID control. The recommended longitudinal controller includes three autopilots: the pitch orientation autopilot which employs the PID control for low angles of attack, the speed control autopilot which designed using PID methods, and MPC for high angles of attack, and the altitude control autopilot. The intelligent hybrid longitudinal controller was effectively simulated and examined. The PID controller showed a strong ability to correct errors associated to control surface actuators, whereas the NNC showed to be robust in handling the overall system response. The actual takeoff trajectory of the aircraft strictly corresponded to the required trajectory, with an extra ability to abort the takeoff if conditions were considered inacceptable.