Abstract
In the research of load simulator control method, PID control is the most widely used control strategy, but PID controller’s three parameters is difficult to set. This paper proposes a BP neural network feedforward PID controller system which uses BP neural network for setting these parameters, and in order to make the network learning speed up the convergence speed and not fall into local minimum, the adaptive vector method is adopted to improve the algorithm. The simulation and experimental results show that this method is good at avoiding the primeval shock and the sine tracking performance of the system has also been improved.
Highlights
The flight control system is an important system of modern aircraft, and its performance plays a decisive role in the performance and safety of the aircraft
Due to the high cost of improving hardware, this paper proposes a feedforward PID controller system which uses BP neural network for setting parameters and adopt an adaptive vector method to improve the algorithm
When only the traditional PID control is used, the output of the system will have a certain degree of lag relative to the input, and the excess force generated during this period cannot be eliminated in time, which has a great influence on the system performance
Summary
The flight control system is an important system of modern aircraft, and its performance plays a decisive role in the performance and safety of the aircraft. The load simulator is often used to test its performance. How to improve the performance of load simulators become a difficult and important point in this field. Researches have been done much on this issue at home and abroad. It can be divided into two aspects: improving hardware or optimizing control strategies. Baghestan K propose a robust control method [2], that is selecting an appropriate correction network to reduce the system sensitivity. Due to the high cost of improving hardware, this paper proposes a feedforward PID controller system which uses BP neural network for setting parameters and adopt an adaptive vector method to improve the algorithm.
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