Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

• Published in: Journal of sensors Vol. 2019; no. 2019; pp. 1 - 15
• Main Authors: Zhao, Libo, Li, Lingling, Jia, Yuan, Gao, Hao, Zhou, X., Yu, Ruifang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; Hindawi Limited
• Subjects: Accuracy; Adaptive control; Agriculture; Aircraft; Artificial neural networks; Back propagation; Back propagation networks; Computer simulation; Control algorithms; Control methods; Controllers; Convergence; Fuzzy control; Fuzzy logic; Kalman filters; Neural networks; Optimization algorithms; Optimization techniques; Parameters; Particle swarm optimization; Proportional integral derivative; Remote sensing; Remote sensing systems; Robust control; Sensors; Stabilized platforms; Three axis stabilization
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2019/5067081

This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.