The Implementation Of Neural Network Pid Flight Control Algorithm Based On Fpga

Neural network control algorithm as a more mature intelligent control algorithm, it has also been a lot of applications in the air to air missile theoretical research, but its practical application is usually implemented through software, while the software implementation with a serial execution, running slow, low reliability. Tt is difficult to meet the actual real-time missile guidance system requirements. The biggest characteristic of Control algorithm for hardware implementation is that it can improve real-time computational speed. In this paper, for missile guidance systems, FPGA-hardware platform for the study of neural network control algorithm for hardware implementation.In this paper, at first, the BP neural network algorithm thinking is in-depth analyzed. And it is inferred to the various stages of BP network in theory. At last, the BP neural network PID flight control algorithm has been studied and summed up, that provides a theoretical basis for hardware implementation. This paper, through the in-depth study and analysis of the above-mentioned theory, puts forward a model which is suitable for the neural network control algorithm for the hardware implementation using FPGA. In this model, the data is applied in serial mode between neural network layers and Layer is applied in parallel operation mode, which can effectively improve the system operation speed. As the modular and hierarchical top-down modular design method can effectively reduce the error, it is the ideal design method to complex large-scale systems. In this paper, this design method is used. System is divided into various module, the individual sub-modules are described with VHDL, and are synthesized and simulated based on QUARTUS II, until it reaches the research design requirements. Finally, the source code is downloaded and deployed to the specific Cyclone II family EP2C70 FPGA chip, which is applied to the study of an actual missile control system. Theoretical analysis and experimental results show that the neural network flight control algorithm for FPGA hardware implementation is effective and feasible to meet real-time requirements. It provides the basis for engineering realization of the actual guidance system.