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Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%.

In this article, in order to solve the trajectory tracking control problem with the drive saturation (thrust overrun) for the 4500-m human occupied vehicle named “Deep-sea Warrior,” a model predictive adaptive constraint control strategy is put forward. The proposed control strategy mainly consists of two controllers. The first part is a kinematics controller designed by quantum-behaved particle swarm optimization model predictive control method. The second part is a dynamic controller designed by an adaptive algorithm. In order to study the effect of the ocean current disturbance on tracking controller, the ocean current is incorporated into the kinematics and dynamics model of the 4500-m human occupied vehicle. The thrusts of four degrees of freedom under the ocean current are calculated from designed controllers. Then, the thrusts are assigned to six thrusters on the 4500-m human occupied vehicle according to its thruster arrangement. An ocean current observer based on artificial fish proportional-integral control is designed for unknown currents. The simulation results of tracking control in three-dimensional underwater environment are given, which illustrates that the proposed control strategy can not only meet the hardware requirements (drive saturation) but also achieve a stable and efficient tracking control performance because of its constraint to speed and speed increment, the effect of the ocean current on kinematics and dynamics models and the dual feedback mechanism.

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.

In this paper, a model predictive control (MPC) method based on quantum-behaved particle swarm optimization (QPSO), combining with sliding mode control (SMC), is studied to solve the dynamic trajectory tracking problem of Unmanned Underwater Vehicle (UUV) in three-dimensional underwater environment. First, the kinematic controller based on QPSO-MPC is designed to obtain the velocity signal. QPSO limits the velocity of UUV within a specified range to handle the speed jump problem when compared with backstepping control. Then kinematic control is extended to dynamic control for verifying the feasibility of QPSO-MPC in the realistic dynamical UUV system. Here SMC is adopted in dynamic control because of its good robustness and anti-interference performance. The combination of kinematic and dynamic controllers can efficiently realize the trajectory tracking of UUV. The experimental simulation results prove that the algorithm proposed in this paper can solve the speed jump and the driven saturation caused by speed jump, and also can satisfy the propeller constraints of UUV.

In this paper, the trajectory tracking control problem with model predictive control approach is investigated for Unmanned Underwater Vehicle (UUV) control. Firstly, the kinematic tracking controller based on model predictive control (MPC) is applied to reach the tracking control in a kinematic way. Compared with conventional backstepping control, MPC can solve the speed jump control problem very well. However, to deal with dynamic tracking control with the nonlinear UUV dynamic model, the computation cost of MPC will be tremendous and cannot meet the real-time requirement. Then, in order to deal with this problem, the MPC algorithm is cooperated with sliding mode control to reach a dynamic tracking control which can be robust with modeling uncertainty and disturbance. Finally, the experimental results show that the proposed method can solve speed jump and robust enough for tracking control compared with other benchmark method.

For the traveling salesman problem (TSP) which is also an important aspect for mobile robots, a continuous Hopfield neural network based on dynamic step is applied to solve TSP. For combinatorial optimization problems such as TSP can be mapped to a Continuous Hopfield neural network (CHNN). The dynamic step size is used to replace the fixed step size, which can solve the problem of the mutual restriction between the convergence precision and the convergence speed. The energy function is designed to represent the path length. The energy of network is constantly updated and converged to a minimum value eventually. Meanwhile, the optimal solution is obtained for TSP. Simulation results show that the proposed algorithm can accelerate the convergence rate and obtain high precision optimization results for TSP.

Trajectory tracking of unmanned underwater vehicles (UUV) in two-dimension is researched by analyzing and establishing a two-dimension kinematic model of UUV. A new approach of trajectory tracking is posed, which is called model predictive control. Model predictive control is used based on linear description of the error model and it has to satisfy the control constraints. The optimization problem of minimizing the objective function is converted to the a quadratic programming problem, which solves the phenomenon of speed jump problem effectively. The simulation experiments show that model predictive control is valid and feasible in solving trajectory tracking of UUV in two-dimension, when compared with the method of backstepping.

Multi-AUV hunting problem has been one of the focusing issues in the multi-robot system research. For the coordination problem between AUVs and ocean current influence in the process of AUV collaboration hunting and in order to hunt the target efficiently, a novel multi-AUV hunting algorithm is proposed in this paper. Firstly, the multi-AUV hunting algorithm based on the bio-inspired neural network is introduced, and the hunting AUVs' paths are guided through bio-inspired neural network. Secondly, ocean current influence for the hunting path is analyzed, and a direction decision-making mechanism is proposed to improve the hunting efficiency. These two important issues are included to make a better solution for multi-AUV hunting. Finally, the simulation results show that the algorithm used in the paper can provide a rapid and high efficient hunting process with obstacles.