Jakarta, INTI - The National Research and Innovation Agency (BRIN), through the Satellite Technology Research Center (PRTS) and the Hydrodynamics Technology Research Center (PRTH), has developed an artificial intelligence (AI)-based ocean wave simulator control system. This control system aims to improve the accuracy of ocean condition simulations to support the development of efficient and sustainable maritime technology.
This research focuses on optimizing the control system using a meta-heuristic optimization approach using the Salp Swarm Algorithm (SSA) combined with a Proportional-Integral-Derivative (PID) controller. This system is implemented on a six-degree-of-freedom motion platform (Stewart Platform), capable of precisely replicating ocean wave dynamics in a simulated environment.
The System is More Cost Efficient
The Head of the Marine and Offshore Structure Technology Research Group at PRTH BRIN, Wibowo Harso Nugroho, explained that the development of this simulator provides a solution to the limitations of direct maritime technology testing at sea.
"Testing process at sea have significant challenges, both in terms of cost and operational complexity. With this simulator, we can accurately replicate wave conditions in the laboratory, allowing for more efficient and controlled research," said Wibowo.
In the developed system, the ocean wave model is converted into a motion trajectory through a trajectory generation process, which is then converted into the movement of the platform legs using the inverse kinematics method. The PID control system, optimized with SSA, then ensures that the platform's movements can follow the wave pattern with minimal error.
SSA Has Lower Error Rate COmpared to Other Methods
Wibowo added that the use of AI-based algorithms provides significant advantages in the control system optimization process. The SSA method allows the researchers to obtain optimal control parameters with a lower error rate than other methods.
The results showed that the SSA method produced the best performance with a lower error value (fitness value) on 16.8 percent compared to the Genetic Algorithm, and 8.7 percent compared to Particle Swarm Optimization. Furthermore, this method can avoid the boundary trapping problem that is common in other optimization methods.
This research found that a simple PID configuration optimized with SSA actually provided better performance than a more complex PID model.
"This shows that the effectiveness of a system does not always depend on its complexity, but rather on the appropriate optimization strategy," Wibowo explained.
Applications for Other Systems
The development of this ocean wave simulator has potential for broad applications, including ship design, offshore technology, and wave compensation systems on marine platforms. Future research will focus on developing nonlinear control methods to improve system performance under more complex dynamic conditions.
This development is part of BRIN's contribution to strengthen research and innovation in the field of national maritime technology, while supporting the development of safer, more efficient and sustainable simulation-based solutions.
Conclusion
The National Research and Innovation Agency (BRIN) has developed an AI-based ocean wave simulator combining the SSA algorithm and PID control to improve the accuracy of laboratory maritime simulations. The system is more efficient than live at-sea testing and has been shown to have a lower error rate than other methods, with potential applications in ship design and offshore technology.
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