Zero-range Orientation Closed-loop Guidance Method Based on Neural Network
For solid-engine aircraft,closed-loop guidance methods with energy matching or angle constraints are generally used in the powered flight segment,which have high control accuracy. However,its accuracy is greatly affected by engine performance deviations. Therefore,a zero-range Orientation closed-loop guidance method based on neural network is proposed to reduce the impact of engine performance deviation on the guidance accuracy. Firstly,the motion model of the powered flight segment of the aircraft is established,and the closed-loop guidance of the zero-range Orientation is analyzed and deduced. Secondly,a multi-input neural network algorithm is designed,the input and output parameters are determined,the residual energy,the velocityto be increased and the angle of the zero-range Orientation are trained. Then,the training results of the above neural network with the zero-range Orientation closed-loop guidance are combined. This method enables feedback of different zero-range Orientation angles under different engine deviations. Finally,different deviation states for simulation verification are chosen. The simulation results show that this method can effectively reduce the influence of engine deviation on the guidance accuracy,and has strong anti-bias ability and high guidance accuracy.
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