Formation control of autonomous surface vehicle and experimental validation

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.

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.

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.

Distributed predictive formation control for autonomous underwater vehicles under dynamic switching topology

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

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.

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.

Unmanned Surface Vehicles (USVs) have widespread commercial, scientific and military applications. In some of these applications station keeping is essential. Since USVs have a bigger surface/ weight ration than large commercial ships, they are more sensitive to wind disturbances, especially during station keeping. The modeling, identification and control of a 4.9 meter long, 180 kilogram wave adaptive modular vehicle (WAM-V) USV are presented in this paper. The propulsion system of the USV is custom designed with two azimuthing thrusters, making the USV fully actuated. System identification tests were conducted to get the precise model of the USV. A sliding mode controller was first applied to the USV and a wind feedforward controller was designed to aid the nonlinear feedback control to mitigate wind disturbance. For the feedforward controller design, a wind model was formulated that uses the apparent wind speed and direction collected from a high accuracy anemometer. A model based adaptive mechanism was developed to tune the feedforward control gains for the best performance. Station keeping field tests were conducted at a location with an average wind speed of 9 knots. The results show that with the tuned feedforward controller, the USV can hold its position and heading better over extended periods of time than a single feedback controller.

Multi-agent formation control is an important part of distributed perception and cooperation, which is convenient to complete various complex tasks and would be a key research direction in the future. This paper reviews the corresponding problems of formation control and the existing centralized and distributed formation control strategies. In particular, we discuss four types of distributed formation control methods based on position and displacement in the global coordinate system and distance and bearing in the nonglobal coordinate system, respectively. Moreover, this paper analyzes affine formation which does not require the global coordinate system. Combined with the current practical applications of multi-agent systems, the latest research for the formation control of the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV) and autonomous underwater vehicle (AUV) is given. Finally, the challenges and opportunities in this burgeoning field are discussed.

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.

In this paper, the formation control problem of unmanned surface vehicles (USVs) is investigated. Unlike the classical formation control problem where the reference signal is required to be second-order differentiable with respect to time, we consider a more general autonomous dynamic system as the reference system. A novel adaptive fuzzy output regulation approach is presented to solve the formation control problem, where a set of regulator equations using only approximation information and a distributed observer are constructed to obtain the feedforward information of the reference system. Based upon this, a distributed adaptive fuzzy control law is designed by using the backstepping technique. It is shown that USVs can effectively achieve the desired formation with the tracking error being adjusted as small as possible. Simulation studies demonstrate that the proposed formation control law is effective and efficient.

A consensus algorithm for coordinated control of unmanned surface vehicles (USVs) is studied. The USVs have different driving directions and initial velocities, so the consensus algorithm is needed to coordinate the directions and velocities of the USVs. Firstly, based on the first order linear model of USVs, get the first order nonlinear differential equations of high frequency, and the simulate environment of USVs. Then, based on the Vicsek model, the consensus algorithm in cooperative control of USVs is proposed. Aiming at the problem that USV's angular velocity lags behind the rudder angle, this paper improves the traditional PD control method, and adopts cascade PID control. So, USV can reach the same driving direction in a shorter time. Finally, simulations prove that the algorithm is feasible and compare the difference of efficiency between the PD controller and the cascade PID controller.

In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.