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This paper discusses the problem of adaptive trajectory tracking control for remotely operated vehicles (ROVs). Considering thruster dynamics, a third-order state space equation is used to describe the dynamic model of ROVs. For the problem of unknown dynamics and partially known input gain, an adaptive sliding mode control design scheme based on RBF neural networks is developed using a backstepping design technique. Because of the saturation constraints of the thrusters, a first-order auxiliary state system is applied, and subsequently, a saturation factor is constructed for designing adaptive laws to ensure the stability of the adaptive trajectory tracking system when the thrusters are saturated. The proposed controller guaranteed that trajectory tracking errors are uniformly ultimately bounded (UUD). Finally, the effectiveness of the proposed controller is verified by simulations. • The thruster dynamics and saturation constraints were considered in controller design. • A third-order state space equation was constructed to describe the dynamic of ROVs. • The unknown dynamics and partially known gain were considered in controller design. • A saturation factor was constructed for the design of the adaptive laws. [ABSTRACT FROM AUTHOR]

The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on depth trajectory tracking control for a Remotely Operated Vehicle (ROV) with dead-zone nonlinearity and saturation nonlinearity of thruster; an adaptive sliding mode control method based on neural network is proposed. Through the analysis of dead-zone nonlinearity and saturation nonlinearity of thruster, the depth trajectory tracking control system model of a ROV which uses thruster control signals as system input has been established. According to the principle of sliding mode control, an adaptive sliding mode depth trajectory tracking controller is built by using three-layer feed-forward neural network for online identification of unknown items. The selection method and update laws of the control parameters are also given. The uniform ultimate boundedness of trajectory tracking error is analysed by Lyapunov theorem. Finally, the effectiveness of the proposed method is illustrated by simulations. [ABSTRACT FROM PUBLISHER]

In this paper, for the human occupied vehicle (HOV) system, a cascaded dynamic tracking controller for thruster fault is proposed in three-dimensional underwater workspace. Firstly, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO) for thruster failure. Secondly, the kinematics controller based on QPSO-model predictive control (MPC) is designed to obtain the speed control signal. QPSO-MPC with speed constrained optimization is proposed to solve speed jump problem caused by thruster failure. Finally, the kinematics controller is used in conjunction with dynamics controller based on the adaptive control to achieve dynamic tracking control which can solve the driving saturation problem caused by thruster failure and tracking disturbance problem by unknown ocean current disturbance. Simulation results showed the proposed dynamic tracking control with fault-tolerant control could realize stable trajectory tracking for thruster failure without the driving saturation in three-dimensional ocean current environment. • The thruster fault-tolerant control method based on QPSO is investigated for HOV. • MPC is employed to realize solution to the speed jump problem. • The speed synthesis method was combined to take the effect of the ocean current into the design of kinematics control law. • For the unknown ocean current, an observer based on adaptive-PI control was designed to estimate it. [ABSTRACT FROM AUTHOR]