Speed Control of Autonomous Underwater Vehicle with Constraints Using Model Predictive Control

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any autonomous vehicle is the control issue to achieve the desired performance. This paper is concerned with speed control of an AUV model respecting the state and control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model Predictive Control (MPC) strategy is proposed to regulate the horizontal speed of AUV to its desired value while the constraints on the states like depth and control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between speed control and depth deviation then quadratic programing technique managed responses to avoid state and control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to control the horizontal speed of AUV while all the constraints on state, control signal and also the variation of the control signal are satisfied.