Motion control of thruster-driven underwater vehicle based on Model Predictive Control

Underwater vehicles have been developed over the last six decades for potential uses in scientific, commercial, environmental, and military purposes, and always utilized to perform difficult tasks in cluttered environments such as deep-sea mining, underwater sampling and oceanic investigations. Since underwater vehicles have nonlinear and highly coupled dynamics, motion control can be difficult when completing complex tasks. This paper describes the implementation of a model predictive controller novel in a class of thruster-driven underwater vehicle. In this paper, a constrained discrete-time model predictive control method is employed for the motion control of thruster-driven underwater vehicles. A nonlinear model in six degree of freedom is established based on the dynamics of human occupied vehicle Jiao-long, and converted into a state space model after practical simplification. To ensure the performance of model predictive controller, a full feedback state observer is established to observe the state. In addition, Hildreth's quadratic programming algorithm is incorporated for solving the optimal control sequence, which greatly reduced the computation of the model predictive control algorithm. In order to show the effectiveness of the motion control method, simulations based on dynamics of human occupied vehicle Jiao-long are conducted. Performance on depth control, heading control and hovering control are evaluated. All the results demonstrate the effectiveness of the method.