Predefined-Time Dynamic Self-Triggered Approximate Optimal Control of Autonomous Surface Vehicles With Disturbances.

This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achieve a consensus task. Then, by using an adaptive extended state observer (AESO) to estimate the total uncertainties and unknown input coefficients, a simplified model-free kinetic controller is designed based on a dynamic surface control (DSC) design. It is proven that the closed-loop system is input-to-state stable The stability of the closed-loop system is established. A salient feature of the proposed method is that a cooperative behavior can be achieved without knowing any priori information. An application to formation control of autonomous surface vehicles is given to show the efficacy of the proposed integrated distributed constant bearing guidance and model-free disturbance rejection control.

Formation control of autonomous surface vehicle and experimental validation

The ability of unmanned surface vehicles (USV) on motion control and the accurate following of preset paths is the embodiment of its autonomy and intelligence, while there is extensive room for improvement when expanding its application scenarios. In this paper, a model fusion of USV and preset path was carried out through the Serret-Frenet coordinate system. Control strategies were then scrupulously designed with the help of Lyapunov stability theory, including resultant velocity control in the presence of drift angle, course control based on the nonlinear backstepping method, and reference point velocity control as a virtual control variable. Specifically, based on USV resultant velocity control, this paper proposes respective solutions for two common scenarios through velocity planning. In a derailment correction scenario, an adaptive reference velocity was designed according to the position and attitude of USV, which promoted its maneuverability remarkably. In a dynamic obstacle avoidance scenario, an appropriate velocity curve was searched by dynamic programming on ST graph and optimized by quadratic programming, which enabled USV to evade obstacles without changing the original path. Simulation results proved the convergence and reliability of the motion control strategies and path following algorithm. Furthermore, velocity planning was verified to perform effectively in both scenarios.

Distributed finite-time performance-prescribed time-varying formation control of autonomous surface vehicles with saturated inputs

Fixed-time adaptive path-following control of autonomous surface vehicles with asymmetric output performance and feasibility constraints

Autonomous surface vehicle (ASV) is developed to fulfill requirement of many offshore applications. One of the examples is tracking an underwater target of interests. Tracking underwater target involves two general steps, target position estimation and target following. In this paper, a control strategy for ASV performing underwater acoustic source tracking is presented. Firstly, method of acoustic source position estimation is discussed. Secondly, mathematical models to represent the ASV kinematic and kinetic are developed. Thirdly, based on the estimated position of the acoustic source and linearized dynamic model, guidance and speed controllers of the ASV are developed to track the position of the acoustic source. The developed controllers are designed based on PID control structure. The performance of the controllers in tracking the acoustic source is demonstrated through simulation results.

Model-based deep reinforcement learning for data-driven motion control of an under-actuated unmanned surface vehicle: Path following and trajectory tracking

Unmanned surface vessels (USVs) are supposed to be able to adapt unstructured environments by means of multi-sensor active perception without any human interference, and high-accuracy path following is achieved for USVs by effective control strategies and intelligent devices of e-navigation. This paper proposes a finite-time predictor line-of-sight (LOS)-based integral sliding-mode adaptive neural (FPISAN) scheme for the path following of USVs in the presence of unknown dynamics and external disturbances, which copies with the problem of merging with the kinematic level and the kinetic level of USVs. From the point of view of USVs’ practical engineering, the inertia matrix of USVs maintains nonzero off-diagonal. In order to ensure that USVs can converge to and follow a defined path, a novel LOS-based guidance law that can acquire sideslip angles by error predictors within a finite time is presented, called finite-time predictor-based LOS (FPLOS). Then, the path-following control laws are designed by combining the neural network (NN) technique with the integral sliding-mode method, where radial basis function NN (RBFNN) is applied to approximate lumped unknown dynamics induced by nonparametric uncertainties and external disturbances. The theoretical analysis verifies that the path-following guidance-control system of USVs is semiglobally uniformly ultimately bounded (SGUUB) with the aid of Lyapunov stability theory. The effectiveness and performance of this presented scheme are illustrated by simulation experiments with the comparison. Note to Practitioners —The design of heading guidance laws and path-following control laws for path following of USVs subject to unknown dynamics and external disturbances is a critical problem, which affects the development of USVs. This problem associated with practical engineering of USVs due to the actual navigation environment that is complex, diversified, and highly unstructured. This paper presents a wholly tight strategy to compensate for unknown sideslip angles and approximate lumped unknown dynamics. Hence, an effective scheme being denoted FPISAN mentioned above is developed for path following of USVs.

The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV’s trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds.

It has been recognized that power control strategy of spectrum sharing for USVs(unmanned surface vessels) counts. The policy for power control of a major USV is proposed to increase profit of spectrum resources. In the environment of an USV sailing on the sea, it is essential for the major USV to communicate effectively with the minor USV. We propose a learning-based power control approach for the major USV to share the joint spectrum with the minor USV. Specifically, the major USV updates its transmitted power abide by a power control policy that is defined beforehand. The minor USV is unaware of the major USV’s condition of transmitting power. The proposed method exploiting the strengths of the Deep Q-Network (DQN), could help the minor USV adjust its transmitting power and make full use of the empty spectrum. Both can transmit information favourably accompanied by the satisfactory service quality. The results of our experiment show that the minor USV communicate with the major USV effectively through our method. Compared to the traditional reinforcement learning method DQN, our method achieves better results.

This paper solves the path following control problem of underactuated unmanned surface vehicle with unknown time-varying disturbances. An adaptive fast non-singular terminal sliding mode path following control strategy is proposed based on an extended state observer (ESO) line-of-sight guidance law. Considering the influence of external disturbances on the navigation of the unmanned surface vehicle, a finite-time convergent disturbance observer (FCDO) is applied in the control law to compensate for the external disturbance to reduce the chatter problem of the sliding mode controller effectively. A robust term is designed to compensate for the estimation error of the observer, which ensures the stability of the system. The results of simulation experiments are carried out to demonstrate that the designed control law has stronger robustness and faster convergence speed.

The rapid development of machine learning in recent years has made it possible to solve the problem of surface ship monitoring. This paper takes intelligent unmanned surface vessel (USV) as the application object. It aims to solve the problem of marine target motion intention recognition based on deep reinforcement learning. Using python to build a highly realistic USV planning platform. Solving the confidentiality of field test data results in the system's simulation results unable to obtain effective feedback from examples. Aiming at the problem of low uniform sampling efficiency of DDPG algorithm, this paper proposes a USV motion control method based on prioritized experience playback DDPG(PER-DDPG) algorithm. This method assists USV to realize the movement intention recognition of the target in the unknown marine environment. This paper also constructs an accurate USV motion model, and carefully sets the USV's action and state space. By setting a new reward function to evaluate the output decision of PER-DDPG, thus strengthening the decision network to control the movement of USV. The experimental results show that the simulation has good realism and environmental adaptability. Compared with the DDPG algorithm, this algorithm has a higher learning efficiency and can realize the recognition of the target movement intention by the USV.

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm.

A tethered flight vehicle such as Subzero III can be a reliable test-bed for AUV control techniques provided that the tether effects be removed by feed-forward control. To achieve the composite control idea, a numerical scheme for prediction of tether effects has been proposed and assessed. We argue that the composite control scheme can also be applied to the control of an ROV during the deployment of the umbilical. The H∞ approach has been applied to the design of the autopilots for autonomous underwater vehicles. The autopilots developed have the following features: (1) they have simpler structures than conventional H∞ design and the performance degrada tion caused by the controller order reduction is negligible; (2) rate feedback is applied for heading and depth control to improve tracking performance; (3) the overshoot specification is considered in the design by choosing suitable weighting functions. The results of both nonlinear simulations and water trials in two different tanks show that the autopilots are robust to model uncertainty, external disturbances and varying dynamics of the vehicle and exhibit good tracking performance. Thus the effectiveness of robust control for AUVs appears promising.

The globally K-exponentially stable path following control of an underactuated autonomous surface vehicle (ASV) is proposed based on the cascaded approach.The dynamics model of the ASV path following errors is derived utilizing the Serret-Frenet coordinate originated at the free path reference point, which creates an added control input, i.e., the change rate of the path parameter, to avoid the singularity problem when adopting the coordinate originating at the orthogonal projection point.The path following reference yaw is designed, and the whole path following model is decomposed into two cascaded sub-models, named the position tracking sub-model and the yaw angle and surge velocity tracking sub-model.The global K-exponential stability of the path following errors is proved using the cascaded system theory with the globally exponentially stable yaw angle and surge velocity tracking controllers.The results of the mathematic simulations and the lake trials of a real ASV demonstrate the performance of the proposed path following control algorithm.