Control System of Ocean Wave Simulator Using PID-Salp Swarm Algorithm

This paper presents a control system optimization of an ocean wave simulator using a meta-heuristic optimization. The proposed control system involves finding leg length trajectories by an Inverse Kinematics (IK) to be used as references for a Proportional-Integral-Derivative (PID). PID gains are tuned using a Salp Swarm Algorithm (SSA) with a Root Mean Square Error (RMSE) of leg position errors as a performance index. A Stewart platform dynamic is modeled in Simscape Multibody, integrated with a trajectory generator, an IK module, and a control system diagram block in Simulink models. The Simulink model of Stewart platform dynamics is then called in the optimization procedure by writing the programming code in MATLAB. Results show that the SSA outperforms other meta-heuristic methods, namely a Genetic Algorithm (GA) and a Particle Swarm Optimization (PSO), achieving the lowest fitness value, 16.8% and 8.7% lower than GA and PSO, respectively. Moreover, the SSA avoids boundary-trapping issues encountered by the PSO, which is stuck at its upper bound. The SSA has successfully enhanced the simplified version of the PID control system, where the scenario of the simplified PID-SSA scenario achieves better tracking error performance than the full PID-SSA configuration. The proposed approach contributes to the advancement of marine simulation technologies, supporting innovation in ocean engineering and sustainable maritime applications.

@article{Machmudah2025,
  title     = {Control System of Ocean Wave Simulator Using PID-Salp Swarm Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Affiani Machmudah and Juchen Li and Mahmud Iwan Solihin and Chiong Meng Choung and Wibowo Harso Nugroho and Ahmad Syafiul Mujahid and Sahlan and Abdul Ghofur},
  doi       = {10.14569/IJACSA.2025.0160970},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160970}
}