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Human Occupied Vehicle (HOV) is one of main tools to investigate submarine resources and carry out some underwater operations, and it can serve as a base for divers when they execute underwater activities. Therefore, the research on HOV is extremely significant. Aiming at the drive saturation (thrust overrun) problem of tracking control which is one of the main research directions, a hybrid control strategy based on quantum-behaved particle swarm optimization-model predictive control (QPSO-MPC) and adaptive sliding mode control (ASMC) is put forward for “Jiaolong” HOV. The hybrid control strategy includes a QPSOMPC-based kinematic controller and an ASMC-based dynamic controller. The kinematic controller transforms the position error into the virtual desired speed. Then the error between the desired speed and real speed is used as the input for the dynamic controller. Through the design of S sliding manifold and dynamic control law, the thrusts of 4 DOF are obtained. The thrusts are distributed to eight thrusters in accordance with thrusters' configuration of “Jiaolong” HOV. So that HOV can be driven to reach and track the known reference trajectory. The innovation of this hybrid control strategy is it takes into account the limited speed for HOV. The constraints on speed and speed increment are added in the optimization part of the kinematic controller. A simple trajectory generator designed by QPSO-MPC is presented with obstacle avoidance behavior. Compared with backstepping control and bioinspired backstepping control, the proposed strategy can deal with the speed jump phenomenon and the drive saturation (thrust overrun) caused by exceeded speed variation. From the simulation results, the hybrid strategy is able to achieve stable and accurate tracking control under the condition of satisfying the physical constraints (speed and thrust constraints). And the trajectory generator is verified to be capable of trajectory planning with obstacle avoidance behavior.
Zhu et al. (Mon,) studied this question.