What are the main challenges and opportunities for improving thruster allocation methods in the future?

1 Challenge 1: Nonlinearities and uncertainties

One of the main challenges for thruster allocation methods is to deal with the nonlinearities and uncertainties that affect the DP system. Nonlinearities are the deviations from the ideal linear behavior of the system, such as saturation, dead-zones, hysteresis, and coupling effects. Uncertainties are the variations and errors in the system parameters, such as environmental disturbances, sensor noise, model mismatch, and thruster faults. These factors can degrade the performance and stability of the DP system, and require robust and adaptive thruster allocation methods that can handle them.

2 Challenge 2: Constraints and objectives

Another challenge for thruster allocation methods is to satisfy the multiple constraints and objectives that the DP system has to meet. Constraints are the limitations that the system has to respect, such as thrust limits, power limits, thruster interactions, collision avoidance, and safety margins. Objectives are the goals that the system has to achieve, such as position accuracy, heading control, fuel consumption, wear and tear, and redundancy. These factors can conflict with each other, and require efficient and flexible thruster allocation methods that can balance them.

3 Challenge 3: Computational complexity and scalability

A third challenge for thruster allocation methods is to cope with the computational complexity and scalability of the DP system. Computational complexity is the amount of time and resources that the thruster allocation methods need to solve the optimization problem. Scalability is the ability of the thruster allocation methods to handle different sizes and configurations of the DP system. These factors can affect the speed and reliability of the thruster allocation methods, and require fast and scalable thruster allocation methods that can run in real-time and accommodate different scenarios.

4 Opportunity 1: Artificial intelligence and machine learning

One of the main opportunities for improving thruster allocation methods in the future is to leverage the advances in artificial intelligence (AI) and machine learning (ML). AI and ML are the fields of computer science that deal with creating systems that can learn from data and perform tasks that normally require human intelligence. AI and ML can offer several benefits for thruster allocation methods, such as learning from past experiences, adapting to changing conditions, finding optimal solutions, and enhancing human decision-making.

5 Opportunity 2: Distributed and cooperative control

Another opportunity for improving thruster allocation methods in the future is to explore the potential of distributed and cooperative control. Distributed and cooperative control are the approaches that involve dividing the control task among multiple agents that communicate and coordinate with each other. Distributed and cooperative control can offer several advantages for thruster allocation methods, such as reducing the computational burden, increasing the robustness, enhancing the flexibility, and enabling the collaboration of multiple vessels.

6 Opportunity 3: Model predictive control and optimization

A third opportunity for improving thruster allocation methods in the future is to apply the concepts of model predictive control (MPC) and optimization. MPC and optimization are the techniques that involve using a mathematical model of the system and a set of criteria to predict and optimize the future behavior of the system. MPC and optimization can offer several benefits for thruster allocation methods, such as accounting for nonlinearities and uncertainties, handling constraints and objectives, and improving the performance and efficiency of the system.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?