Fuzzy self-adjusting proportion integration differentiation for eppo quadrocopter

Abstract: As quadrocopters can fly stably and be controlled flexibly so that they could fulfill the requirements for seeding, farmland information acquisition and ultra-low-volume spraying. Conventional PID control algorithm could hardly resist the environmental perturbation as structural parameters changed. In this study, a fuzzy proportion, integration and differentiation (PID) control algorithm was proposed to improve the robustness of a plant protection quadrocopter when the load changed during the operation. In the fuzzy PID control algorithm, the errors of the attitude angle and the angular speed were detected and imported into the fuzzy rule table, where correction amount was then calculated and used to correct the initial PID parameters. The updated attitude angle and the angular speed could meet the requirements of the system with a better static and dynamic performance during flight. A quadrocopter control system using STM32 Micro Control Unit (MCU) as the core processor was designed based on the study of the flight theory of aircrafts and the methods of attitude determination. The attitude heading reference system (AHRS) module was adapted as a real-time solver to determine the aircraft attitude parameters so that the flight attitude could be controlled by fuzzy PID algorithm. The matlab-simulink software was used in this study to simulate the conventional PID and fuzzy PID control algorithms and the simulation results were analyzed and compared. For the conventional PID algorithm, the overshoot of the system was 41.9% with the rising time of 0.78 s. With the fuzzy PID control algorithm, the overshoot of the system was 28.6% and the rising time was 0.69 s. With fuzzy PID control algorithm, the overshoot of the system decreased 13.3% and the rising time reduced 0.09 s compared with the conventional PID algorithm with the scaling factor as 5, integral coefficient as 0.03, differential coefficient as one and system gain as one. Using other parameters have also led to similar results, which indicated that fuzzy PID control algorithm had a better control performance. Moreover, experiments were conducted to verify the simulation results. The results showed that, the system performed in a stable way under a small load with the control of the fuzzy PID algorithm; however, this cannot be achieved by using the conventional PID algorithm under same condition. With the conventional PID algorithm, the overshoot of the system was 37.5% and the rising time was 0.62 s with a heavy load. While, the overshoot of the system was 22.5% and the rising time was 0.57s when the fuzzy PID control algorithm was adopted. The overshoot of the system decreased 15.0% and the rising time reduced 0.05 s, which agreed well with the simulation results. The results showed that the fuzzy PID algorithm had a stronger adaptability with easier adjustment of working parameters and can lead to quicker dynamic response capability and more stability of the system when compared with the conventional PID algorithm. The performance and disturbance rejection ability of the plant protection quadrocopter were significantly improved by using the proposed fuzzy PID algorithm. This study can provide a reference for the research of plant protection aircraft control algorithm.