Search Results (14)

To address the issues of external unknown disturbances and roll motion in the tracking control of underactuated unmanned surface vehicle (USV) formation, a cooperative formation control method based on nonlinear model predictive control (NMPC) algorithm and finite-time disturbance observer is proposed. Initially, a tracking error model for the USV formation is established within a leader–follower framework, utilizing a four-degree-of-freedom (4-DOF) dynamic model to simultaneously account for roll motion and trajectory tracking. This error model is then approximately linearized and discretized. To mitigate the initial non-smoothness in the desired trajectories of the follower USVs, a tracking differentiator is designed to smooth the heading angle of the leader USV. Thereafter, a quasi-infinite horizon NMPC algorithm is developed, in which a terminal penalty function is constructed based on quasi-infinite horizon theory. Furthermore, a finite-time disturbance observer is developed to facilitate real-time estimation and compensation for unknown marine disturbances. The proposed method’s effectiveness is validated both mathematically and in simulation. Mathematically, closed-loop stability is rigorously guaranteed via a Lyapunov-based proof of the quasi-infinite horizon NMPC design. In simulations, the algorithm demonstrates superior performance, reducing steady-state tracking errors by over 80% and shortening convergence times by up to 75% compared to a conventional PID controller. These results confirm the method’s robustness and high performance for complex USV formation tasks. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

This paper proposes a control method that combines a fractional-order sliding mode observer and a cooperative control strategy to address the problem of path-following for underactuated autonomous underwater vehicles (AUVs) in complex marine environments. First, a fractional-order sliding mode observer is designed, combining fractional calculus and double-power convergence laws to enhance the estimation accuracy of high-frequency disturbances. An adaptive gain mechanism is introduced to avoid dependence on the upper bound of disturbances. Second, a formation cooperative control strategy based on path parameter coordination is proposed. By setting independent reference points for each AUV and exchanging path parameters, formation consistency is achieved with low communication overhead. For the followers’ speed control problem, an error-based expected speed adjustment mechanism is introduced, and a hyperbolic tangent function is used to replace the traditional arctangent function to improve the response speed of the system. Numerical simulation results show that this control method performs well in terms of path-following accuracy, formation maintenance capability, and disturbance suppression, verifying its effectiveness and robustness in complex marine environments. Full article

Autonomous Underwater Vehicles (AUVs) have shown significant promise across various underwater applications, yet face challenges in dynamic environments due to the limitations of traditional motion planning methods while Artificial Potential Field (APF)-based control barrier functions focus solely on obstacle proximity and distance-based methods oversimplify obstacle geometries, and both fail to ensure safety and satisfy turning radius constraints for under-actuated AUVs in intricate environments. This paper proposes a robust Model Predictive Control (MPC) framework integrating an enhanced fast marching control barrier function, specifically designed for AUVs equipped with fully directional sonar systems. The framework introduces a novel improvement for moving obstacles by extending the control barrier function field propagation along the obstacle’s movement direction. This enhancement generates precise motion plans that ensure safety, satisfy kinematic constraints, and effectively handle static and dynamic obstacles. Simulation results demonstrate superior obstacle avoidance and motion planning performance in complex scenarios, with key outcomes including a minimum safety margin of 1.86 m in cluttered environments (vs. 0 m for A* and FMM) and 1.76 m in dynamic obstacle scenarios (vs. 0.13 m for MPC-APFCBF), highlighting the framework’s ability to enhance navigation safety and efficiency for real-world AUV deployments in unpredictable marine environments. Full article

This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the application condition was that the vehicle was symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches. Full article

In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme. Full article

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy. Full article

This paper considers a trajectory-tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North-East-Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method for which the application condition is that the vehicle is symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches. Full article

This paper proposes an algorithm that performs the task of tracking the desired trajectory for underactuated marine vehicles (primarily underwater) that move horizontally. The control scheme, which takes into account model inaccuracies and external disturbances, was designed using the quantities obtained after the transformation of the dynamic equations of motion resulting from the decomposition of the inertia matrix. This, in turn, led to the equation of dynamics with a diagonal inertia matrix. A specific feature of the offered controller is its dual role. It not only allows tracking the desired trajectory, but at the same time, makes it possible to estimate the impact of dynamic couplings when the vehicle is in motion. Such an approach to the tracking task is important at the initial design stage when the choice of the control algorithm has not yet been decided and experimental tests have not been performed. This is feasible because the new variables after the velocity transformation include not only vehicle parameters, but also actual velocities and forces. Therefore, it is also possible to track the original variables. The theoretical results were followed up with simulation tests conducted on a model with three degrees of freedom for two underwater vehicles. Full article

With the increasing number of applications for both surface and underwater autonomous vehicles, a great amount of control methods and guidance principles has been developed over the years. This work proposes a review of the most common of these methods. It is mainly focused on model-based nonlinear control methods and guidance principles. Notably, this work details examples and variations of model-based linearizing controllers, applications of line of sight guidance, sliding mode controllers and several other less common control methods for both fully-actuated and underactuated vehicles. Additionally, this work proposes an alternative definition of underactuation with respect to the task allowing for a better understanding of the consequences of underactuation on control. Comparison of fully-actuated and underactuated cases shows how control laws can be used to solve the problems of underactuation and what mechanisms can be used to compensate for the lack of actuation on a degree of freedom. The reviewed methods are compared and discussed with respect to their capabilities, limitations and suitability for typical tasks. Full article

This paper investigates the trajectory tracking control problem for underactuated underwater vehicles, for which a model is expressed in terms of quasi-velocities arising from the inertia matrix decomposition. The control approach takes into account non-modeled dynamics and external disturbances and is suitable for symmetric vehicles. It is shown that such systems can be diagonalized using inertial quasi-velocities (IQVs). The strategy consists of the velocity controller and two adaptive integral sliding mode control algorithms. The proposed approach, introducing velocity transformation and using backstepping methods and integral sliding mode control, allows trajectory tracking for vehicles in described models with symmetric inertia matrix. Proof of the stability of the closed system was carried out using IQV. The proposed scheme has been verified on two 3 DOF models of underwater vehicles with thruster limitations. A brief discussion of the results is also given. Full article

In this work, an application of the trajectory tracking algorithm proposed in the literature for underactuated marine vehicles is presented. The main difference relies on that here the dynamics of the vehicle are expressed in terms of some quasi-velocities (QV). This fact has a double meaning. First of all, it is shown that using the QV, it is possible to control a vehicle in the absence of one variable because the works related to marine vehicles have only concerned fully actuated systems. In addition, a controller using QV provides information that gives some insight into vehicle dynamics and that is not available in classical equations of motion. The simulations done on two 3-DOF models of different underwater vehicles and using two desired trajectories show performance of the considered control strategy. A discussion of the presented control scheme and selected control approaches from recent years was also conducted, and the benefits of the proposed approach were pointed out. Full article

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better. Full article

To promote the development of military and civilian applications for marine technology, more and more scientific research around the world has begun to develop unmanned surface vehicles (USVs) technology with advanced control capabilities. This paper establishes and identifies the model of vector propulsion USV, which is widely used at present. After analyzing its actuator distribution, we consider that the more realistic vessel model should be an incomplete underactuated system. For this system, a virtual control point method is adopted and an adaptive sliding mode trajectory tracking controller with neural network minimum learning parameter (NNMLP) theory is designed. Finally, in the simulation experiment, the thruster speed and propulsion angle are used as the inputs of the controller, and the linear and circular trajectory tracking tests are carried out considering the delay effect of the actuator, system uncertainty, and external disturbance. The results show that the proposed tracking control framework is reasonable. Full article