Optimization of Model Predictive Control Horizons Using Particle Swarm Algorithm to Synchronize Marine Simulator Motion

Marine simulators are effective tools for making a ship feel like driving by creating a similar environment using motion commands. The main problem with simulators is the limited workspace which does not allow them to generate accurate real-time floating movements, so they require a motion synchronization algorithm. Recently, the use of predictive control has become popular in marine simulators. Values of control horizon and future forecast affect the computational load. However, because the designer manually selects these horizons, they are lower than the optimal level. In this paper, a new method based on particle swarm algorithm was used to achieve the best control and forecast horizons by minimizing some periods such as sensory error, displacement and computational load. The proposed method eliminates the disadvantages of the MPC-MCA method such as time-consuming empirical estimation through trial and error for initial control and forecast horizons, while minimizing optimal cost performance and computational load. The simulation results showed the efficiency of the proposed method based on the improvement of performance output and computational load.