Distributed model-free formation control of networked fully-actuated autonomous surface vehicles.

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

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.

This article addresses the predefined-time optimal motion control problem of an autonomous surface vehicle (ASV) with disturbances under dynamic self-triggered frameworks via reinforcement learning (RL). Initially, to eliminate the influence of disturbance on the ASV, a predefined-time second-order integral sliding mode control (SOISM) strategy is formulated by establishing a novel integral sliding mode (ISM) function and a terminal sliding mode function. Subsequently, a predefined-time approximate optimal motion (AOM) control strategy is further developed to ensure the ASV maintains a stable state. Furthermore, a single critic network is used to obtain an approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. The above two strategies are established under the dynamic self-triggered framework, which relies on the current information to predict the next updating time, effectively reducing the computational and communication burden while avoiding the continuous monitoring of the ASV state. In the theoretical analysis, the main challenges lie in the design of Lyapunov functions and triggered conditions to ensure the stability of the sliding mode dynamics and the disturbed ASV. By applying the Lyapunov stability principle and designing two novel Lyapunov functions and triggered conditions that both contain dynamic variables, we demonstrate that the developed control strategies can ensure the stability within the specified time frame. Ultimately, simulation results verify the efficacy of the proposed motion control approach.

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

Formation control of autonomous surface vehicle and experimental validation

Reinforcement learning-based affine formation maneuver control of autonomous surface vehicles with safety constraints under time-varying actuator faults

Cooperative steering of the captain and automated system can effectively reduce the necessity of extremely accurate environment perception of intelligent surface vehicles (ISVs), and enhance the safety and fault-tolerance of decision-making and motion control. This paper presents a human-machine shared line-of-sight (LOS) path following control method of under-actuated ISVs in complex sea environments with dynamic and static obstacles. At the kinematics level, a human-machine shared LOS guidance law is presented based on a dynamic authority mechanism determined by a shared weighted coefficient. The proposed guidance law not only preserves the simplicity and interpretation of the LOS guidance, but also enables the ISVs to combine the complementary strengths of the captain and the automated system. At the kinetic level, an adaptive extended state observer (AESO) is developed to estimate model uncertainties and environmental disturbances, and a model-free kinetic control method is constructed based on the AESO. By using the input-to-state stability theory, uniformly and ultimately bounded of the closed-loop system is analyzed. The simulation results are provided to validate the effectiveness of the human-machine shared LOS path following control method.

Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation

This paper investigates the event-triggered train formation control problem for multiple autonomous surface vehicles (ASVs) formation system in polar communication interference environment. Firstly, a distributed resilient guidance algorithm is introduced to generate the reference route based on waypoints. In the guidance algorithm, the distributed resilient leader predictor (RLP) is applied to obtain the states of ice-breaking ship when communication fails, and the resilient train formation scheme is designed to compute the reference signals for ASVs. Subsequently, an adaptive neural event-triggered train formation control algorithm is developed. In the control algorithm, the neural networks (NNs) are conducted to approximate model uncertainties, and event-triggered control (ETC) is employed to minimize controller updates. Furthermore, the threshold of the event-triggered mechanism (ETM) can be dynamically adjusted by states of system. It is proved that the formulated algorithm can ensure the prediction errors converge and multiple ASVs system is stable in polar communication interference environment. Finally, two simulation experiments are adopted to illustrate the effectiveness 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.

In this paper, a surge speed tracking problem for an unmanned surface vehicle (USV) driven by a direct current (DC) motor is investigated. The surge velocity tracking controller is developed based on a modular neural dynamic surface control (MNDSC) design method. Specifically, two neural-network-based identifiers are designed to identify the uncertainties existing in surge dynamics, propeller and DC motor. Then, a robust adaptive surge speed controller based on a dynamic surface control design method is designed by using the recovered unknown information from identifiers. The proposed surge velocity tracking controller is able to track any time-varying bounded velocity profiles. The stability of the closed-loop system is established based on cascade theory and input-to-state stability (ISS) theory. The effectiveness of the proposed surge speed controller for the USV is illustrated via simulations.

This paper focuses on Cooperative Adaptive Cruise Control (CACC) in autonomous vehicles. In CACC, vehicles regulate their speed according to a preceding “leader” vehicle in the lane, forming a platoon. In a benign environment, CACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, CACC is vulnerable to various security threats. In this paper, we consider one of the critical threats, where the platoon leader is compromised, and forges acceleration information sent to platoon members. Such attack would lead to traffic instability and potential collisions. First, we propose information sharing in CACC model to allow vehicles and fixed infrastructure to sense and share information about platoon leaders, hence improves the reliability and supports the detection of anomalous behavior. Then, we propose a real-time anomaly detection mechanism that combines statistical learning with the physics laws of kinematics. Specifically, we propose Generalized Extreme Studentized Deviate with Sliding Chunks (GESD-SC) approach, which is applied at each vehicle in the platoon to detect anomalies in real-time based on the vehicle's own speeding decisions. Kinematic model is also utilized to detect unexpected deviations using the leader's information, communicated directly and observed by the leader's neighboring vehicle(s) and/or supporting infrastructure. Combining kinematic model with GESD-SC has shown to be effective in detecting falsification attacks in CACC. Furthermore, we analyze the time performance, and show that the proposed technique outperforms existing method in detection accuracy and processing time.

Long-term unmanned surface vehicle (USV) is an effective means in ocean environmental monitoring and target detection for extended periods. Efficient steering control is the basis and premise for long-term USV to improve the adaptability and the endurance ability in complex sea states. In this paper, an efficient control algorithm is developed with the aid of fuzzy inference system and dynamic surface control technology. Specifically, based on data mining technology the fuzzy inference system is employed to identify the unknown yaw dynamics for the USV in presence of sea environmental disturbance and measurement noises. Besides, a robust adaptive steering law is established by using the dynamic surface control (DSC) design approach in Lyapunov stability analysis. In simulation, the simulation object is the independent designed “Lanxin” USV of Dalian Maritime University in China. Simulation results are given to validate the efficacy of the proposed method.

This article presents a design method for the three-dimensional trajectory tracking control of an underactuated autonomous underwater vehicle with unknown current disturbances. To simplify the complexity of the controller and avoid the singular problem induced by initial state constraints, a novel nonlinear backstepping technique based on virtual control variables is employed to design the kinematics and dynamics controllers. The control law is developed by building virtual errors, which can solve the problem of differential explosion in the traditional backstepping. Specifically, an ocean current observer based on the kinematics model is proposed to estimate unknown current disturbances, where the estimation is integrated into the autonomous underwater vehicle kinematics and dynamics equations. The convergence of tracking errors and system stability are proven by using Lyapunov stable theory. Finally, the simulation studies were provided to illustrate the effectiveness and good performance of the above trajectory tracking strategy.

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.