This study presents a photo-driven micro-lever fabricated to multiply optical forces using the two-photon polymerization 3D-microfabrication technique. The micro-lever is a second class lever comprising an optical trapping sphere, a beam, and a pivot. A micro-spring is placed between the short and long arms to characterize the induced force. This design enables precise manipulation of the micro-lever by optical tweezers at the micron scale. Under optical dragging, the sphere placed on the lever beam moves, resulting in torque that induces related force on the spring. The optical force applied at the sphere is approximately 100 to 300 pN, with a laser power of 100 to 300 mW. In this study, the optical tweezers drives the micro-lever successfully. The relationship between the optical force and the spring constant can be determined by using the principle of leverage. The arm ratio design developed in this study multiplies the applied optical force by 9. The experimental results are in good agreement with the simulation of spring property.

In this paper, we propose a robust PID controller tuning method for parametric uncertainty systems (or interval plant family) using fuzzy neural networks (FNNs). This robust controller is based on robust gain and phase margin (GM/PM) specifications that satisfy user requirements. Here, the FNN system is used to identify the relation between the PID controller parameters and robust GM/PM. We can use the trained FNN system to determine the parameters of the PID controllers in order to satisfy robust GM/PM specifications that guarantee robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust controller scheme.

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