Efficient multi-laser PBF microstructural tuning via physics-based feedforward control

The increasing deployment of robotic systems in industrial applications has driven widespread use of two-link robots, valued for their high speed and precision. However, their inherent nonlinear dynamics and strong coupling effects present substantial challenges to achieving high-precision trajectory tracking. To address these issues, this paper proposes a feedforwardPID control strategy optimized using a hybrid Genetic AlgorithmSequential Quadratic Programming (GASQP) approach. The proposed method combines the anticipatory capabilities of feedforward control with the corrective feedback of PID control, enabling automatic and efficient parameter tuning. Simulation results demonstrate that, in comparison to conventional PID control, the proposed approach enhances trajectory tracking accuracy by approximately 39.61%. Specifically, the GASQP-optimized controller reduces the Root Mean Square Error (RMSE) to 0.48mm for an Archimedean spiral trajectory, and further to 0.01mm for a Sine-like trajectory, confirming its adaptability across various trajectory profiles. Torque analysis further highlights the complementary interaction between feedforward and PID components, substantiating the methods effectiveness. These results underscore the proposed strategys potential to significantly improve trajectory tracking accuracy and robustness for two-link robots, especially in complex dynamic environments.

Efficient tuning for motion control in diverse systems: a Bayesian framework