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The article was motivated by the design of adaptive control algorithms for the control of a a fixed wing unmanned aerial vehicle (UAV). An adaptive system is a system that, with its structure or parameters, adapts to changes in the behavior of the object and based on the knowledge of variable properties, maintains the quality of its regulatory transition. The knowledge gained on this small UAV can be applied to larger aircraft. The creation of the proposed adaptive control into the UAV consisted of the creation of a simulation model of the aircraft based on known physical laws, the properties of the aircraft and a mathematical description. An adaptive PID controller for stabilization with changing coefficients based on the airspeed of the aircraft was designed and simulated. A validated control of the mathematical model of an unmanned aircraft was designed and simulated using the methods of estimation and identification of the UAV model parameter based on measured data from flight tests. Identifying dynamic parameters is a challenging task due to several factors, such as random vibration noise, interference, and sensor measurement uncertainty. The designed adaptive UAV control provides very promising results in improving the controllability of the aircraft while reducing the effect of speed changes on the stability and controllability of the system compared to the conventional PID controller. The comparison was performed on three selected types of PID controllers. The first type had fixed coefficients for the entire range of speeds calculated using the Control toolbox in MatLab. The second type also had constant coefficients over the entire range of speeds calculated using the Naslin method. The third adaptive type of PID controller had variable coefficients based on approximate polynomials dependent on the change in flight speed. The reason for the comparison was to show an increase in margin of stability using the method of variable coefficients of the PID controller based on the change of flight speeds. The obtained results show that the proposed adaptive control algorithm is robust enough to control the movement of the aircraft in the longitudinal plane and due to the introduced process and measurement errors, while the used Kalman filter effectively eliminates these errors.

Adaptive Control and Estimation of the Condition of a Small Unmanned Aircraft Using a Kalman Filter