Author(s)

Daoyang Zhu, Da Fang, Yan Ma, Shaoli Duan

Corresponding Author

Daoyang Zhu

ABSTRACT

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.

KEYWORDS

Flight simulation, PID control, BP neural network, self-tuning control