Enhanced Transient Performance of Micro-Robotic Systems via SFOA-Based PID Tuning

Precise position control of magnetically actuated micro-robotic systems is challenging due to viscous damping, parameter sensitivity, and the requirement for high transient accuracy without overshoot. This study presents an optimization-based control framework for enhancing transient and steady-state performance of a magnetically actuated micro-robotic position control system. The system dynamics are modeled based on magnetic force and viscous drag interactions on paramagnetic micro-particles suspended in fluid, yielding a second-order representation suitable for controller design. A proportional–integral–derivative (PID) controller regulates particle position, with parameters optimally tuned using the starfish optimization algorithm (SFOA), a bio-inspired metaheuristic that balances global exploration and local exploitation through biologically motivated movement, preying, and regeneration mechanisms. The tuning process is guided by the integral of squared time multiplied by squared error performance index, which suppresses late-stage tracking errors and promotes smooth transient behavior. Simulation studies are conducted for a 1000 µm step reference input, with the optimization independently repeated 25 times to assess robustness and consistency. Results demonstrate strong convergence reliability, with tightly clustered objective function values and low standard deviation across runs. Time-domain analyses confirm that the SFOA-tuned PID controller achieves a smooth, monotonic, overshoot-free response with the shortest rise and settling times compared to PID controllers tuned by arithmetic optimization, jellyfish search optimization, Harris Hawks Optimization (HHO), and a hybrid arithmetic–rat swarm approach. These findings establish the proposed method as an effective and reliable solution for high-precision micro-robotic positioning where overshoot-free operation and consistent transient performance are critically important.