This paper studies the trajectory tracking control problem of an autonomous underwater vehicle (AUV). We develop a novel Lyapunov-based model predictive control (LMPC) framework for the AUV to utilize computational resource (online optimization) to improve the trajectory tracking performance. Within the LMPC framework, the practical constraints, such as actuator saturation, can be explicitly considered. Also, the thrust allocation subproblem can be addressed simultaneously with the LMPC controller design. Taking advantage of a nonlinear backstepping tracking control law, we construct the contraction constraint in the formulated LMPC problem so that the closed-loop stability is theoretically guaranteed. Sufficient conditions that ensure the recursive feasibility, and hence the closed-loop stability, are provided analytically. A guaranteed region of attraction is explicitly characterized. In the meantime, the robustness of the tracking control can be improved by the receding horizon implementation that is adopted in the LMPC control algorithm. Simulation results on the Saab SeaEye Falcon model AUV demonstrate the significantly enhance trajectory tracking control performance via the proposed LMPC method.

The path-following (PF) problem of an autonomous underwater vehicle (AUV) is studied, in which the path convergence is viewed as the main task while the speed profile is also taken into consideration as a secondary task. To accommodate the prioritized PF tasks, a novel multiobjective model predictive control (MPC) (MOMPC) framework is developed. Two methods, namely, weighted sum (WS) and lexicographic ordering, are investigated for solving the MOMPC PF problem. A logistic function is proposed for the WS method in an attempt to automatically select the appropriate weights. The Pontryagin minimum principle is subsequently applied for the WS-MOMPC implementation. The implicit relation between the two methods is shown, and the convergence of the solution with the MOMPC PF control algorithms is analyzed. Simulation studies on the Saab SeaEye Falcon AUV demonstrate the effectiveness of the proposed MOMPC PF control.

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.

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 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.