Research and Modeling on Self-tuning Dynamic Control System of Flight Simulator Based on PID Neural Network
The accuracy of PID control system is a little poor on Stewart platform, and parameters adjustment of PID controller cannot be adjusted in real time online, by combining the advantage of BP neural network, which is good self-learning, self-adaptation, and it can approach any nonlinear system with arbitrary precision. The closed loop error between directly measured output and desired value of system, and desired length is chosen to be the input of controller ,in order to achieve self-tuning real-time online of controller parameters for motion platform, improve the accuracy of control system at the same time. The dynamic simulation model of flight simulator based PID neural network is built and utilized to verify the reliability in Matlab/Simluink. The result shows that the PID neural network control algorithm can achieve adaptive parameter adjustment, and system error have dropped significantly after neural network training. It is proved that the PIDNN controller can achieve self-tuning real-time online of the parameter, and the system has good adaptability and robustness.
flight simulation PID control BP neural network self-tuning control
Daoyang Zhu Da Fang Yan Ma Shaoli Duan
Wuhan Technical College of Communications, Wuhan, Hubei 43000, China
国际会议
成都
英文
1-6
2018-10-30（万方平台首次上网日期，不代表论文的发表时间）