A Model Predictive Adaptive Control Algorithm for Human Occupied Vehicle In order to solve the trajectory tracking problem of underwater submersible in the ocean current environment, a model predictive adaptive control method is proposed. The proposed control strategy is mainly composed of two parts: model predictive control and adaptive control. Model predictive control can add kinematic constraints (speed and speed increment constraints) to the controller to obtain the relative desired speed, which is the position tracking system. Because of its ability to change the parameters with the environment, adaptive control is very suitable for the dynamic control of underwater submersible in the ocean current environment, which is the speed tracking control system. Then the whole trajectory tracking process is completed. Lastly, the proposed control strategy is applied to the Deepsea Warrior human occupied vehicle to verify the tracking effectiveness of it in the ocean current environment.

Path Following Based on Waypoints and RealTime Obstacle Avoidance Control of an Autonomous Underwater Vehicle This paper studies threedimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Lineofsight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the realtime obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUVxe2x80x99s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes realtime obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.