Abstract
In order to resolve the external disturbances and model parameters uncertainty during the unmanned aerial vehicles flight process, in this paper, we designed the H ∞ /S-plane controller for longitudinal control of unmanned aerial vehicles (UAVs). The S-plane control model with strong nonlinearity was used as the outer loop controller and the robust Hoc control algorithm with strong robustness was used as the inner loop controller. The effectiveness of the H ∞ /S-plane controller in the longitudinal control of unmanned aerial vehicles without external interference, with external interference and parameter perturbation, was simulated by a certain UAVs nominal model. The results showed that compared with H ∞ /PD controller and PID controller, H ∞ /S-plane controller has faster response speed and stronger anti-interference ability. So H ∞ /S-plane controller is more suitable for the longitudinal control of unmanned aerial vehicles than H ∞ /PD controller and PID controller.
Highlights
With the need of application and the development of aviation technology, unmanned aerial vehicles (UAVs) as a new type of intelligent equipment has been widely concerned and applied in military and civil fields
In order to verify the effectiveness of the controller proposed in this paper, a certain UAV was selected as the research object and compared with H∞/PD controller and PID controller for comparative simulation test
The results show that the velocity fluctuation of the H∞/S-plane controller is small and can reach the original stable state quickly, indicating that the control system has strong anti-interference ability and fast self-stabilization ability
Summary
With the need of application and the development of aviation technology, unmanned aerial vehicles (UAVs) as a new type of intelligent equipment has been widely concerned and applied in military and civil fields. UAVs have the advantages of low loss, low cost, a lower casualty rate, easy maintenance and maneuverability, etc They can be used in harsh and dangerous environments to accomplish tasks that are difficult for manned aircraft. The commonly used UAV longitudinal control methods include PID control, incremental dynamic inverse control, fuzzy control [7], neural network control [8], adaptive control approaches and so on. Neural network control has strong robustness and nonlinear fitting ability, but the control algorithm is complex, and it is easy to lose information in the control process. The H∞/PD model control and PID control effects are compared to verify the rapidity, accuracy, robustness and dynamic performance of H∞/S-plane model control
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