压电快速反射镜的神经网络滑模控制OA

To meet the demanding requirements of high-precision alignment and tracking in laser communi-cation systems operating under complex environmental conditions,this paper presented a novel control al-gorithm for a piezoelectric-actuated fast steering mirror(FSM).A Hammerstein-structured nonlinear model incorporating a Prandtl-Ishlinskii hysteresis representation was established for the piezoelectric actu-ator,and system linearization was achieved through a geometrically derived inverse model employed as a feedforward compensator.For the linearized system,a hybrid control strategy integrating radial basis func-tion(RBF)neural networks with sliding mode control was designed,where the RBF network approximat-ed unmodeled nonlinear parameters within the nonsingular fast terminal sliding mode control framework to enhance robustness while maintaining tracking precision.Experimental validation on a servo platform con-firms the algorithm's effectiveness in tracking composite-frequency trajectories with a root mean square er-ror below 2.7 μrad.Under 10%model mismatch conditions,the method maintains a root mean square er-ror under 7.3 μrad when tracking 50 Hz sinusoidal signals,representing performance improvements of ap-proximately 40.16%and 75.9%over conventional nonsingular fast terminal sliding mode control and ac-tive disturbance rejection control,respectively,while demonstrating strong robustness against parameter perturbations.