Event-Triggered Adaptive Control Design With Prescribed Performance for Macro-Micro Composite Positioning Stage

This article investigates the rapid vibration reduction problem for macro-micro composite positioning stage (MMCPS), which is of great engineering significance for microelectronics manufacturing. The MMCPS is modeled as a nonstrict feedback interconnected nonlinear system. Based on backstepping technique and radial basis function neural networks, an event-triggered adaptive control scheme with prescribed performance is developed. By designing performance function and coordinate transformation, the expected state convergence rate and precision are obtained. The relative threshold event-triggering mechanism is adopted to decrease the update frequency of control input. It is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results are conducted to illustrate the effectiveness of the control algorithm.

Automated summary

This article presents an event-triggered adaptive control scheme for rapid vibration reduction in macro-micro composite positioning stage, modeling the system as a nonstrict feedback interconnected nonlinear system. The proposed control algorithm utilizes backstepping technique and radial basis function neural networks to improve state convergence rate and precision, while reducing control input update frequency using relative threshold event-triggering mechanism. Simulation results demonstrate the effectiveness of the control algorithm in practice.