Abstract
Aiming at the characteristics of strict nonlinearity, strong coupling and instability of quadrotor UAV, a fuzzy neural network PID controller based on a hybrid particle swarm algorithm (PSO-GA) is designed. The global optimization ability is improved by integrating the crossover and mutation operations of the genetic algorithm into the particle swarm optimization algorithm. In this paper, the initial value of each parameter of fuzzy neural network is optimized offline by PSO-GA algorithm and adjusted online by gradient descent method. The optimized PID controller can be applied to the attitude control of the quadrotor UAV. The simulation results show that the system has faster response speed, stronger robustness, less steady-state error and stronger tracking ability than the traditional control algorithm.
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Li, X., Zhang, S., Zheng, F., Wang, B. (2020). Fuzzy Neural Network PID Control of Quadrotor Unmanned Aerial Vehicle Based on PSO-GA Optimization. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_32
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DOI: https://doi.org/10.1007/978-981-15-0474-7_32
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