Advanced autopilot design with extremum-seeking control for aircraft control

The aim of this research is to enhance adaptive autopilots for the effective management of aircraft systems, control maintenance, and the rejection of external disturbances. To achieve this objective, we propose the design of an autopilot integrated with the extremum-seeking control (ESC) algorithm. Although autopilots proficiently manage the lateral and longitudinal modes of aircraft control, they lack filtering or adaptive capabilities, thereby exposing the system to significant external threats. To mitigate these risks, the ESC method is employed. This adaptive approach can operate in a disturbance rejection manner by adjusting parameters for unknown inputs and restoring the system to its original controlled response. ESC represents a versatile control method suitable for effective application in simulations or experimental models. Through the incorporation of this method, the pitch attitude hold autopilot, altitude hold autopilot, and yaw autopilot acquire advanced disturbance rejection capabilities with adaptive ESC features. The novelty of the proposed method lies in providing advanced disturbance rejection properties to conventional autopilots, thereby rendering them innovative and superior disturbance rejection controllers. The newly developed autopilots are capable of eliminating severe disturbances from the system response, including ramp, sinusoidal, and step disturbances. The integration of autopilots with ESC offers significant advantages, such as superior disturbance rejection properties for the aircraft unmanned aerial vehicle (UAV) system. The proposed method successfully eliminates severe disturbances, as demonstrated in simulation results, surpassing previous methods in effectiveness. Furthermore, the Autopilot-ESC method enhances aircraft operation even under disturbances, minimizing energy consumption and ensuring stability and control. This novel method reduces operator workload and ensures reliable and efficient autonomous flight capabilities. Additionally, the adaptability of the Autopilot-ESC to changing environmental conditions make it well-suited in aircraft UAVs. This upgraded version of autopilot surpasses other robust controllers, such as Linear Quadratic Gaussian (LQG) regulator and Model Predictive Control (MPC), as it can effectively address ramp, sinusoidal, and step disturbances, which LQG and MPC cannot handle.