基于PSoC架构的多旋翼飞行器LSTM-ASMC融合算法研究OA

This paper addresses the issues of chattering and response lag in multi-rotor aircraft under complex wind field conditions by proposing a neural network-enhanced adaptive sliding mode control(ASMC)method based on a PSoC hardware platform.The method replaces the fixed disturbance upper-bound estimation model in traditional adaptive sliding mode control with a long short-term memory network,enabling dynamic optimization of the sliding mode parameter λ and the switching gain ρ.Furthermore,an efficient deployment of the algorithm is achieved using the heterogeneous computing architecture of PSoC with an integrated NPU.Hardware acceleration performance tests demonstrate that the PSoC hardware platform requires only 81.7 μs for single-sample processing,supporting high-frequency closed-loop control at 12.24 kHz.Simulation results indicate that,compared to ASMC,the proposed method reduces the convergence time of the system pitch/roll angles from 0.62 s to 0.27 s,suppresses the roll angle chattering amplitude from±1.66° to±0.14°,and limits the maximum movement speed is to±1.1 m/s.