This article focuses on the trajectory tracking control of under-actuated unmanned surface vehicles subject to unknown ocean current and input quantization. Regarding kinematics, we devise an extended-state-observer-based guidance law capable of compensating for ocean currents to track the intended trajectory. Concerning kinetics, we propose an event-triggered adaptive fuzzy quantization control law using a linear analytical model to depict input quantization, eliminating the need for prior quantization parameter information. A notable aspect is the reduction in both execution frequency and magnitude, thereby mitigating communication burdens. The stability of this control strategy is proofed through input-to-state stability analysis. Simulation experiments are conducted to affirm the viability of the event-triggered adaptive fuzzy quantization control strategy.

In this paper, a finite set model predictive control method based on data-driven neural network predictors (DNNPs) is proposed for pulse width modulation (PWM) rectifiers with fully unknown parameters. First, DNNPs are structured based on concurrent learning such that model uncertainties and input gains are identified simultaneously. Secondly, based on the information estimated by the predictors, a finite set model predictive power controller is designed, which is responsible for simplifying the rolling optimization and reducing the computational complexity. Finally, the stability analysis is provided based on input-to-state stability theory, and simulation results are provided to prove the effectiveness of the proposed method.

This article addresses cooperative target enclosing of underactuated autonomous surface vehicles (ASVs) subject to obstacles. Each ASV suffers from input constraints in addition to unknown kinetics induced by model nonlinearities, unknown input gains, and external disturbances. A safety-critical cooperative target enclosing control method is proposed for surrounding a maneuvering target vehicle. Specifically, a finite-time fuzzy predictor is presented to learn the unknown kinetics with the integral of historical vehicle data. By using a distributed target estimator to recover the target position, a nominal distributed target enclosing control law is developed to achieve a circumnavigation formation. To avoid collisions between ASVs and obstacles/team members, input-to-state safe high-order control barrier functions are first introduced for encoding safety constraints. Based on the safety constraints and input constraints, a quadratic programming problem is formulated, and an optimal safety-critical control law is obtained by using projection neural networks to track the optimal solution. The closed-loop control system is proven to be input-to-state stable via Lyapunov theory. Moreover, the multiple ASV systems are proven to be input-to-state safe regardless of high-order relative degree. The salient contributions of the proposed approach lie in finite-time fuzzy learning and collision-free target enclosing control under disturbances. Simulation results validate the effectiveness of the proposed safety-critical model-free control method for cooperatively surrounding a maneuvering target.

This article investigates the yaw angle tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. A model-free adaptive antidisturbance autopilot control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real-time and historical data, a data-driven concurrent learning extended state observer (CLESO) method is designed to estimate the unknown ASV model parameters and ensure the convergence of the estimation without requiring persistent excitation. Then, a model-free yaw angle tracking controller is designed based on the data-driven CLESO method. Through Lyapunov stability analysis, the closed-loop system is proven to be input-to-state stable. Simulation and experimental results validate the effectiveness of the proposed CLESO method for the yaw angle tracking of an ASV with fully unknown dynamic model.

This paper presents a finite control set model free predictive control method for doubly fed induction generator for direct current generation system. The system model is fully unknown, and only the input and output data are measured. Firstly, data-driven neural network predictors are proposed to reconstruct the system model. Filtered data of system states are recorded and used to update the neural network parameters. The system of the data-driven NN predictors is proved to be input-to-state stable. Then, a finite control set model free predictive controller is designed based on the perdiction model. The optimal control actions are selected based on the receding horizon optimization with a finite control set. Simulation results show the effectiveness of the proposed model free predictive control of doubly fed induction generator based on data-driven neural network predictors.

This article addresses the distributed formation control of multiple under-actuated autonomous surface vehicles (ASVs) in a receding-horizon setting. The ASVs are subject to physical constraints, in addition to stationary and moving obstacles. A barrier-certified distributed model predictive control method is proposed with the capability of avoiding collision with stationary and moving obstacles and neighboring ASVs. Specifically, a data-driven neural predictor is used to learn unknown functions in ASV kinetics. A nominal distributed receding-horizon position control law is developed based on the learned unknown function to achieve the desired formation within physical constraints. To ensure the safety requirement, a barrier-certified control law is designed based on control barrier functions to generate the signals of optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed based on the data-driven neural predictor to track the desired heading signals. Constrained quadratic programming problems are formulated based on barrier functions for barrier-certified distributed formation control and solved via neurodynamic optimization using one-layer recurrent neural networks. Thus, the proposed control method is able to ensure obstacle avoidance in the formation control of multiple ASVs in the presence of stationary and moving obstacles. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified distributed model predictive control method for ASV formation.

This article addresses the path-guided flocking control of unmanned surface vehicles (USVs) suffering from fully unknown kinetics. A model-free learning and anti-disturbance control method is developed to achieve path-guided flocking without using prior knowledge of model nonlinearities, ocean disturbances, or control input gains. Specifically, data-driven concurrent learning extended state observers (CLESOs) based on fuzzy systems are presented to estimate the unknown kinetics of USVs. With the proposed CLESO, a model-free path-following control law is proposed for a leader USV to follow a parameterized path. Then, model-free flocking control laws based on potential functions are proposed for follower USVs to avoid collisions and maintain network links within available communication ranges. Through cascade stability analysis, the closed-loop system is proven to be globally asymptotically stable. Simulation results substantiate the proposed CLESO-based anti-disturbance control approach for path-guided flocking of a swarm of USVs.

This article deals with the problem of network-based path-tracking control of an underactuated unmanned surface vehicle subject to model uncertainties and unknown disturbances over a wireless network. A two-level network-based control architecture is proposed, including a local inner loop and a remote outer loop. In the remote outer loop, an event-triggered line-of-sight guidance law is designed to achieve path tracking while reducing the network burden for the remote control at the kinematic level. In the local inner loop, an extended state observer is employed to estimate the unknown disturbances due to the model uncertainties and environmental disturbances. Based on the estimated information from the extended state observer, an event-triggered anti-disturbance control law is developed to reduce the execution rate of actuators at the kinetic level. The stability of the closed-loop path-tracking system is proved based on the input-to-state stability and cascade stability theory. The effectiveness of the proposed network-based method for path tracking of the USV is verified via experiments.

This article addresses an output-feedback flocking control problem for a swarm of autonomous surface vehicles (ASVs) to follow a leading ASV guided via a parameterized path. The leading and following ASVs are subject to completely unknown model parameters, external disturbances, and unmeasured velocities. A data-driven adaptive anti-disturbance control method is proposed for establishing a flocking behavior without any prior knowledge of model parameters. Specifically, a data-driven adaptive extended state observer (ESO) is proposed such that unknown input gains, unmeasured velocities, and total disturbance are simultaneously estimated. For the leading ASV, an output-feedback path-following control law is developed to follow a predefined parameterized path. For following ASVs, an output-feedback flocking control law is developed based on an artificial potential function for collision avoidance and connectivity preservation, in addition to a distributed ESO for estimating the velocity of the leading ASV through a cooperative estimation network. The simulation results are discussed to substantiate the efficacy of the proposed path-guided output-feedback ASV flocking control based on data-driven adaptive ESOs without measured velocity information.

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models.

This article addresses the distributed path maneuvering of underactuated unmanned surface vehicles (USVs) with collision avoidance and connectivity preservation. The USVs are guided by the multiple virtual leaders moving along the multiple parameterized paths and only a fraction of USVs have access to the virtual leaders. An observer-based finite-time control method is proposed to achieve a containment formation. Specifically, a finite-time extended state observer is employed to recover the unmeasured linear/angular velocities and estimate the total disturbances consisting of model uncertainties as well as ocean disturbances at first. Then, observer-based finite-time guidance laws using the information of neighbors are designed based on a containment scheme. An artificial potential field is incorporated into the distributed guidance law design to avoid collision and preserve connectivity. Finally, antidisturbance kinetic control laws are devised based on the finite-time convergent observers and nonlinear tracking differentiators. It is proven that all error signals in the closed-loop system are ultimately uniformly bounded, and the distributed path maneuvering formation pattern can be achieved in a finite time when USVs outside the collision avoidance and connectivity preservation region. Simulation results are given to verify the effectiveness of the proposed output feedback control method for the distributed path maneuvering of multiple USVs with position-yaw measurements only.

This paper addresses the cooperative path following the problem of ring-networked under-actuated autonomous surface vehicles on a closed curve. A cooperative guidance law is proposed at the kinematic level such that a symmetric formation pattern is achieved. Specifically, individual guidance laws of surge speed and angular rate are developed by using a backstepping technique and a line-of-sight guidance method. Then, a coordination design is proposed to update the path variables under a ring-networked topology. The equilibrium point of the closed-loop system has been proven to be globally asymptotically stable. The result is extended to the cooperative path following the lack of sharing of a global reference velocity, and a distributed observer is designed to recover the reference velocity to each vehicle. Moreover, the cooperative path following the presence of an unknown sideslip is considered, and an extended state observer is developed to compensate for the effect of the unknown sideslip. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed cooperative guidance law for the path following over a closed curve.

This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due to the unknown velocities. Next, an estimation model based on a single-hidden-layer neural network is developed to approximate the unknown follower dynamics induced by uncertain model parameters, unmodeled dynamics, and environmental disturbances. A bounded control law is designed based on the neural estimation model and a saturated function. The salient feature of the proposed controller is twofold. First, only the measured line-of-sight range and angle are used, and the velocity information of the target is not required. Second, the control torques are bounded with the bounds known as a priori. The input-to-state stability of the closed-loop system is analyzed via cascade theory. Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target.

This paper is concerned with the cooperative path maneuvering of multiple underactuated autonomous surface vehicles (ASVs) moving along a parameterized path. Each vehicle is subject to uncertain kinematics and unknown kinetics induced by model uncertainty and ocean disturbances. A modular adaptive control method is presented to develop the cooperative controller such that a queue formation along the parameterized path can be achieved. First, two identification modules are developed such that the uncertain kinematics and unknown kinetics can be estimated by an adaptive term and echo state networks, respectively. Next, a cooperative path maneuvering controller module is designed based on a line-of-sight guidance scheme, tracking differentiators, and a path variable containment approach. The path variable containment approach is used to guarantee that the ASVs are evenly spaced between two virtual leaders moving along the path. Finally, the stability of the identification-controller pair is analyzed. Comparative studies are performed to validate the efficiency of the proposed method.

This paper considers cooperative dynamic positioning (CDP) of multiple offshore vessels in the presence of dynamical uncertainties, time-varying ocean disturbances and unmeasured velocity, aimed at collectively holding a relative formation and reaching a reference position. K-filter observers are first designed to estimate the unmeasured velocity information of each vessel, and then observer based CDP controllers are developed with the aid of dynamic surface control (DSC) technique, neural network and iterative learning approach. The formation among vehicles can be guaranteed if the graph induced by the vessels and the reference point contains a spanning tree. It is proved by Lyapunov analysis that the proposed control laws can ensure that all the signals in the closed-loop systems are uniformly ultimately bounded, and tracking errors converge to a small neighborhood of origin.