This paper addresses the problem of high-speed waypoint tracking and real-time obstacle avoidance for large-scale underactuated autonomous underwater vehicles (AUVs) in the vertical plane. Specifically, a robust nonlinear model predictive control (RNMPC) scheme is proposed, considering different types of constraints including the scale of the AUV, the finite field of view of the sensor, the input saturation, the physical limits on system state, and the influence of the vertical underactuated velocity. To navigate in the completely unknown environment with nonconvex obstacles, a dynamic sensing and collision avoidance scheme is proposed so that the collision avoidance can be properly formulated into convex constraints in the RNMPC optimization problem. Recursive feasibility and closed-loop stability are proved rigorously. Through the high-fidelity simulations with graph and data visualization techniques, the proposed algorithm has higher waypoint tracking accuracy, safer obstacle avoidance ability, and better multiple constraints handling capability than the existing dynamic virtual AUV (DVA) technique.Note to Practitioners—This article was motivated by the problem of high-speed waypoint tracking and obstacle avoidance for large-scale underactuated autonomous underwater vehicles (AUVs) in an unknown environment. In practical engineering, the obstacles’ useful information (e.g., shapes and positions) cannot be directly reflected by the raw data (distance or acoustic image) of multi-beam sonars. And the multi-beam sonars are also limited by detection range and angle. Another practical problem is that such type of AUV suffers from different constraints including the scale of the vehicle, the physical limits on system poses and velocities. In addition, the influence of the underactuated velocity cannot be ignored due to the high speed of the vehicle. Considering the above practical engineering problems, we design a robust nonlinear model predictive control (RNMPC) scheme from the kinematic level of the AUV to achieve high-speed waypoint tracking and obstacle avoidance. And then we developed a dynamic sensing and collision avoidance scheme to formulate nonconvex collision avoidance into convex constraints in the RNMPC optimization problem. We demonstrate the effectiveness of the proposed control method in a high-fidelity simulation environment with large size terrain and shipwrecks as obstacles. In this environment, the AUV can dynamically read the distance data from obstacles to the sensor through the virtual multi-beam sonar equipped in the head of the vehicle, which can simulate the actual engineering experiment with high-fidelity. This work could be applied to other underactuated systems, such as unmanned aerial vehicles, unmanned surface vehicles, etc. In future research, we will consider more complex practical working cases, such as the presence of dynamic obstacles in an unknown environment and the disturbance of current.

In this article, the dynamic positioning (DP) control problem of constrained unmanned marine vehicles with thruster faults is investigated. To deal with the system constraints and thruster faults effect simultaneously, a codesign framework based on integral sliding mode control (ISMC) and model predictive control (MPC) is proposed. The main design challenge comes from the multiplicative uncertainty introduced by thruster faults, which may cause failures in the DP control with traditional MPC. To solve this problem, we propose to use an improved tube-based MPC in which a fault uncertain set can be characterized and the terminal set containing the fault factor can be derived. Combining the ISMC we show that, all possible trajectories of the unmanned marine vehicle are constrained in the tube for all possible realizations of uncertainties. Finally, the simulation results demonstrate the effectiveness of the proposed codesign framework.

This article aims to develop a robust model predictive control (MPC) scheme for the trajectory tracking control of autonomous underwater vehicles (AUVs) subject to bounded disturbances. Based on the error dynamics model derived from the AUV dynamics and the desired trajectory, an improved tube-based MPC scheme is then developed. The tube-based MPC solves two optimal control problems, the first solves a standard problem for the nominal system which defines a reference state trajectory, and the other attempts to steer the state of the disturbed system to stay in a tube centered around the reference state trajectory thereby enabling robust control of the AUV systems. For tube-based nonlinear MPC, finding a local linear feedback to characterize the tube is challenging. To address it, we replace the local linear feedback controller with an ancillary one that incorporates the tightening constraints to ensure the disturbed system state stays in the online optimized tube. The simulation results demonstrate the effectiveness of the proposed method.

This paper attempts to develop an integrated guidance and control scheme for obstacle avoidance of a large-scale underactuated autonomous underwater vehicle (LUAUV) with high speed in unknown complex environments. Under a finite field of view of the environmental perceiving sensor, a novel guidance algorithm based on tracking differentiator and receding horizon optimization is proposed to generate a smooth guidance signal, respecting the physical limits on the system state including pitch attitude, velocity, and acceleration. To track the guidance signal and the preset forward velocity accurately, a hierarchical control strategy with kinematics and dynamics levels is raised. At the kinematics level, a robust model predictive control (RMPC) is employed for the vehicle to track the guidance signal and produce a virtual pitch velocity signal. At the dynamics level, an adaptive fast integral terminal sliding mode controller is developed based on the actuated dynamic model of the LUAUV with dynamic uncertainties, matched disturbances, and mismatched disturbances. It can be guaranteed that the tracking errors of the virtual pitch velocity and preset forward velocity locally converge to zero in finite time. Through the high-fidelity visual simulations, the proposed scheme has higher precision, faster single-step solution speed, and stronger robustness than the conventional MPC.

This paper presents a fault-tolerant control method based on the integral sliding mode (ISM) output feedback technique for the dynamic positioning control of an unmanned marine vehicle (UMV) subject to signal quantization and thruster faults. By using output information and estimates of thruster effectiveness factors, a novel integral sliding manifold is first constructed to maintain that the sliding function of system output equals the one of quantized output, which removes the region restriction that exists in the literature. Then based on the quantized signals of system output, a compensator is designed to introduce some degrees of design flexibility. To conquer the quantization effects, we incorporate a dynamic quantization parameter adjustment strategy and the fault-tolerant technique into the ISM output feedback control algorithm. The relation between the attraction area and quantization range has been revealed for the first time. Finally, the closed-loop stability can be guaranteed from every beginning time in despite of the quantization and thruster faults. Simulation through a typical floating production ship has verified the effectiveness of the proposed method.

This article studies the formation tracking problem of a team of autonomous underwater vehicles (AUVs) with the ocean current disturbances. A distributed Lyapunov-based model predictive controller (DLMPC) is designed such that AUVs can keep the desired formation while tracking the reference trajectory, despite the presence of external disturbances. The DLMPC inherits the stability and robustness of the extended state observer (ESO)-based auxiliary control law and invokes online optimization to improve formation tracking performance of the multi-AUV system. The closed-loop stability of the multi-AUV system is guaranteed by the stability constraint that utilizes the ESO-based auxiliary controller and the associated Lyapunov function. Furthermore, the inter-AUV collision avoidance can be achieved by incorporating well-designed artificial potential fields-based cost term in the formation tracking cost function. Extensive simulations on the Saab Falcon AUVs are carried out, demonstrating the superior control performance and robustness of the proposed method.