激光跟踪测量系统线性自抗扰控制OA

Laser tracking measurement systems frequently operate under complex disturbances,including nonlinear friction and inter-axis coupling,which induce response delays and degrade control accuracy.To address these issues,a radial basis function neural network-optimized linear active disturbance rejection control(RBF-LADRC)method is proposed.In this approach,external disturbances and model uncertain-ties are aggregated as a total disturbance within the LADRC framework.A linear extended state observer is employed to estimate this total disturbance in real time and compensate for it online.Furthermore,a ra-dial basis function neural network is introduced to identify the Jacobian of the controlled plant online.Based on the identified Jacobian,a gradient descent algorithm is constructed to enable adaptive updating of controller gains.The stability and parameter convergence of the closed-loop system are established using discrete Lyapunov theory.Experimental validation on a laser tracking measurement system demonstrates that,compared with conventional LADRC,the proposed method enables dynamic adaptation of controller parameters to varying operating conditions.The system bandwidth is increased by approximately 12％,the settling time is reduced by about 32％,and the root mean square tracking error of the laser trajectory is decreased by approximately 16％.The proposed RBF-LADRC method does not rely on an accurate sys-tem model and effectively enhances both dynamic performance and control precision,indicating strong po-tential for engineering applications.