Underactuated Autonomous Surface Vehicles Research Articles

Bearing-driven approach for unknown target tracking by multi-unit uncertain underactuated marine autonomous surface vehicles

In this study, we successfully address the formation tracking challenge for underactuated autonomous surface vehicles (ASVs) in uncertain environments, despite the lack of direct target information. Leveraging only relative angle measurements, we develop a control scheme that ensures the closed-loop error trajectory remains globally uniformly ultimately bounded. The bound achieved is influenced by the precision of the dynamic controllers and the effectiveness of the adaptive mechanism, highlighting the robustness and adaptability of our approach. This work provides a practical solution for ASV formation tracking in real-world scenarios.

Learning-Based Guidance and Control Codesign for Underactuated Autonomous Surface Vehicles: Theory and Experiment

Learning-Based Guidance and Control Codesign for Underactuated Autonomous Surface Vehicles: Theory and Experiment

Event-Triggered Adaptive PID Fault-Tolerant Control of Underactuated ASVs Under Saturation Constraint

This article discusses a control problem of underactuated autonomous surface vehicles (ASVs) subject to internal/external uncertainties, input saturations, and actuator faults, and proposes a novel event-triggered adaptive PID fault-tolerant (ETAPID-FT) control solution. To resolve the design difficulty caused by the underactuated feature, a standard multivariate integral cascade form regarding the mathematical model of underactuated ASVs is established. And then, to facilitate the implementation of PID-based design framework, the nonsmooth actuator saturation nonlinearity caused by the saturation constraint is represented by a smooth saturation model. In the control design, considering full features of the actuator fault and smooth function, an indirect neural approximation with single-parameter-leaning technique is developed, which endows the function of self-tuning control gain for the control solution. To relieve the mechanical wear of actuator, an improved event-triggered protocol by implanting a decreasing function of ASV’s position error is proposed. Moreover, the theoretical results show that the proposed ETAPID-FT control solution can guarantee the boundedness of all the signals in the closed-loop control system of underactuated ASVs. Finally, the validity of the developed control solution is confirmed by the simulation and comparative results

Safety-Critical Containment Maneuvering of Underactuated Autonomous Surface Vehicles Based on Neurodynamic Optimization With Control Barrier Functions.

This article addresses the safety-critical containment maneuvering of multiple underactuated autonomous surface vehicles (ASVs) in the presence of multiple stationary/moving obstacles. In a complex marine environment, every ASV suffers from model uncertainties, external disturbances, and input constraints. A safety-critical control method is proposed for achieving a collision-free containment formation. Specifically, a fixed-time extended state observer is employed for estimating the model uncertainties and external disturbances. By estimating lumped disturbances in fixed time, nominal containment maneuvering control laws are designed in an Earth-fixed reference frame. Input-to-state safe control barrier functions (ISSf-CBFs) are constructed for mapping safety constraints on states to constraints on control inputs. A distributed quadratic optimization problem with the norm of control inputs as the objective function and ISSf-CBFs as constraints is formulated. A recurrent neural network-based neurodynamic optimization approach is adopted to solve the quadratic optimization problem for computing the forces and moments within the safety and input constraints in real time. It is proven that the error signals in the closed-loop control system are uniformly ultimately bounded and the multi-ASVs system is guaranteed for input-to-state safety. Simulation results are elaborated to substantiate the effectiveness of the proposed safety-critical control method for ASVs based on neurodynamic optimization with control barrier functions.

Safety-Critical Game-Based Formation Control of Underactuated Autonomous Surface Vehicles

Dear Editor, This letter addresses the noncooperative formation control of multiple underactuated autonomous surface vehicles (ASVs) in an obstacle-laden environment. Each ASV is subject to external disturbances and fully unknown model parameters. A safety-critical game-based formation control method is proposed such that the multiple ASVs are able to track a reference trajectory, and accomplish each individual interest in safety behaviors simultaneously. The stability and safety analyses show that the closed-loop system is input-to-state stable (ISS), and the multi-ASV system is guaranteed for input-to-state safety (ISSf). Simulation results substantiate the proposed safety-critical game-based formation control method.

Constrained Control of Autonomous Surface Vehicles for Multitarget Encirclement via Fuzzy Modeling and Neurodynamic Optimization

This article addresses the cooperative multitarget encircling control of underactuated autonomous surface vehicles with unknown kinetics subject to velocity and input constraints. A distributed observer is designed for the vehicles to estimate the geometric center of the area covered by multiple moving targets. Based on the target center estimate, a multitarget encircling guidance law is developed to form encircling trajectories around the targets. A data-driven fuzzy predictor is designed for learning the vehicle kinetics, including model input gains, with available data. Based on the learned model, a nominal control law is developed to track reference guidance signals. In order to satisfy the velocity and input constraints, a feasibility condition for velocities is derived based on a control barrier function, and a neurodynamics-based optimal control law is developed based on the feasibility condition and input constraint. The bounded input-to-state stability of the closed-loop control system is theoretically proved. Simulation results are elaborated to substantiate the effectiveness of the proposed control approach for circumnavigating multiple moving targets.

Heading and velocity guidance based path following of autonomous surface vehicle with uncertainty attenuation and asymmetric saturated constraints

The path following of underactuated autonomous surface vehicle (ASV) with line-of-sight (LOS)-based heading and velocity guidance is studied thoroughly in the presence of complex uncertainties and asymmetric input saturation that actuators are likely to suffer from. On the basis of the extended-state-observer-based LOS (ELOS) principle and guided velocity design strategies, a finite-time heading and velocity guidance control (HVG) scheme is presented. Firstly, an improved ELOS (IELOS) is developed such that the unknown sideslip angle can be estimated directly, instead of requiring one more step to calculate it by the output of observers and relying on the equivalent assumption between actual heading angle and guidance angle. Secondly, a new form of velocity guidance is designed by considering magnitude and rate constraints and path’s curvature, keeping in line with ASV’s manoeuvrability and agility. Then asymmetric saturation is considered and studied by designing projection-based finite-time auxiliary systems to avoid parameter drift. All error signals of the closed-loop system of ASV are forced to converge to an arbitrarily small neighbourhood of the origin within a finite settling time by the HVG scheme. The expected performance of the presented strategy is demonstrated via a series of simulations and comparisons. In addition, to show the strong robustness of the presented scheme, stochastic noises modelled by Markov process, bidirectional step signals and faults both multiplication and addition types are considered in simulations.

GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments

This paper addresses the target tracking problem of an under-actuated autonomous surface vehicle (ASV) subject to internal uncertainties and exogenous forces in ocean environments with static and dynamic obstacles. A collision-free target tracking (CFTT) control method is proposed based on a guiding vector field (GVF) guidance and a super-twisting algorithm (STA) control. Specifically, a resultant GVF is developed to guide the ASV towards the target position and to avoid multiple obstacles. Based on the resultant GVF, a saturated guidance law is designed to calculate the surge velocity and angular velocity commands. Then, robust exact differentiator (RED)-based observers are developed to estimate the lumped disturbances within finite time. By using the estimated lumped disturbances, STA-based anti-disturbance kinetic control laws are designed to track the given velocity commands within finite time. The finite-time input-to-state stability of the closed-loop system is proven based on cascade theory. Emulational and experimental validations are conducted to substantiate the efficacy of the proposed CFTT control method.

Barrier-Certified Distributed Model Predictive Control of Under-Actuated Autonomous Surface Vehicles via Neurodynamic Optimization

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.

Bearing-based formation control for multiple underactuated autonomous surface vehicles with flexible size scaling

Most existing formation control approaches for Autonomous surface vehicles (ASVs) focused on achieving rigid patterns with invariant inter-vehicle distance in open ocean environment. However, these methods cannot be applied to the cooperative missions on irregular inland waterway with changing width (such as rivers, canals and fjords), wherein the inter-vehicle distance should be continuously adjustable to respond to the environment dynamically. This paper proposes a bearing-based formation scaling control scheme for underactuated ASVs to achieve the desired patterns but with a flexible size scaling. By assuming that the fleet satisfies a leader–first-follower structure, the formation scaling parameters are accessible only to leaders, while each follower needs to maintain the bearing constraints to its neighboring ASVs without any knowledge of scale information. We respectively design distributed control laws for leaders and follower, and adaptive neural networks (NNs) are incorporated to approximate the dynamic uncertainties induced by parameter perturbations, external disturbances and input saturations. To improve the tracking performance, an auxiliary adaptive law is resorted to compensate the non-zero estimation error of NNs. Theoretical analysis demonstrates that the closed-loop scaling maneuver tracking and formation tracking errors converge to zero asymptotically. The comparative simulation results are presented to substantiate the effectiveness of the designed control method.

Bearing-based prescribed-time formation control of underactuated autonomous surface vehicles with the interception of attackers

This paper studies the bearing-based prescribed-time formation keeping control problem of collaborative underactuated autonomous surface vehicles (ASVs) systems subject to malicious attacks, model uncertainties and external disturbances. For the leader-follower formation control structure, the leaders usually serve as pacemakers and play dominating role in generating the reference trajectory. On this account, it is imperative to ensure the safety of the leaders when attacks occur. To prevent the breakdown of the formation system from attacks, our control strategy mainly focuses on the following two points: 1) assigning defenders from the collaborative underactuated ASVs system to intercept the oncoming attackers, and 2) ensuring the formation keeping maneuvers of the remaining ASVs to be isolated from the influence arising from the interception mission. With these in mind, a defender allocation algorithm is developed to switch the formation keeping control mode to the interception control mode smoothly when the attackers get close to the leaders. Meanwhile, a bearing-based formation interception control strategy is developed, in which a formation keeping control scheme and an interception control scheme are designed under a unified framework by sharing the same control form and different tracking objectives. Besides, for the sake of a faster convergence rate of the resulting closed-loop system, a dynamic control gain is introduced into the formation keeping control scheme to endow the formation keeping system with a prescribed-time convergence capability. Numerical simulations are conducted to illustrate the efficacy of our approach.

Cooperative Target Enclosing of Ring-Networked Underactuated Autonomous Surface Vehicles Based on Data-Driven Fuzzy Predictors and Extended State Observers

This article addresses the cooperative target enclosing problem of ring-networked underactuated autonomous surface vehicles (ASVs). The target velocity is unavailable, and the ASVs are subject to sideslip effects, unknown control gains, and uncertain kinetics. The control objective is to drive a fleet of ASVs to surround a moving target at a desired range and maintain a spaced formation. An integrated distributed guidance and model-free control method is presented based on extended state observers (ESOs) and a data-driven fuzzy predictor. Specifically, by using two ESOs to estimate the unknown relative kinematics induced by the unknown target velocity and unknown sideslip and a distributed target estimator to recover the target position, intermediate range keeping and phase keeping guidance laws are designed to achieve a circular motion and an evenly spaced formation, respectively. Next, a model-free fuzzy control law is developed based on a data-driven fuzzy predictor, which learns the unknown control gains and uncertain kinetics simultaneously. Finally, the closed-loop control system is proven to be input-to-state stable through Lyapunov analysis. The salient feature of the proposed method is that cooperative circumnavigating a maneuvering target with unknown velocity can be achieved without the global target information and knowledge of vehicle kinetics. Simulation results validate the effectiveness of the proposed distributed guidance and control method for cooperative target enclosing of ASVs.

Safe cooperative path following with relative-angle-based collision avoidance for multiple underactuated autonomous surface vehicles

This paper addresses the safe cooperative path following for multiple autonomous surface vehicles (ASVs) in the presence of static and dynamic obstacles. The positions and velocities of the obstacles are unknown, and only the relative angle is measured. The ASVs are subject to uncertain kinetics and unknown disturbances induced by wind, waves and currents. Switching cooperative guidance laws with collision avoidance are designed at the kinematic level, and a collision avoidance strategy based on the relative angle is introduced into the desired yaw rate design. A smooth switch between the cooperative path following and the collision avoidance is achieved by using an exponential-function-based weighted coefficient. At the kinetic level, nonlinear switching extended state observers (NSESOs) based on fractional power functions are developed to estimate the model uncertainties and environmental disturbances. Anti-disturbance kinetic control laws are designed based on the nonlinear switching extended state observers and finite-time convergent differentiators. A salient feature of the proposed cooperative path following method is that both the static and dynamic obstacles can be avoided by using the relative angle only. Through theoretical analysis, it is proven that the closed-loop system is input-to-state stable, and a safe distance can be maintained between the ASVs and the obstacles. Simulation and hardware-in-loop (HIL) experiment results are given to substantiate the effectiveness of the proposed safe cooperative path following control method for multiple ASVs based on relative angles.

Weather optimal area-keeping control for underactuated autonomous surface vehicle with input time-delay

The problem of the underactuated Autonomous Surface Vehicle (ASV) area-keeping control under unknown slow time-varying external environment disturbances and input time-delay, an integral time-delay sliding mode control algorithm based on weather optimal intermittent strategy is proposed in this article. Firstly, integral time-delay sliding mode control law is constructed, with controller parameters chosen according to the upper bound of time-delay. In the control law, a geometric updating rule is designed for the virtual suspension point, which enables ASV at a particular small area with a weather optimal heading. And, weather optimal intermittent strategy is developed to extend the area-keeping control method, while reducing ASV energy consumption. Besides, the closed-loop system is globally asymptotically stable through Lyapunov-Krasovskii analysis. Finally, the performance of the proposed algorithm is illustrated through the simulation examples.

Bearing-Based Formation Maneuver Control of Multiple Underactuated Autonomous Surface Vehicles With Leader-First Follower Structure

Bearing-Based Formation Maneuver Control of Multiple Underactuated Autonomous Surface Vehicles With Leader-First Follower Structure

Distributed Path Following of Multiple Under-Actuated Autonomous Surface Vehicles Based on Data-Driven Neural Predictors via Integral Concurrent Learning

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.

Swarm velocity guidance based distributed finite-time coordinated path-following for uncertain under-actuated autonomous surface vehicles

This article mainly researches the problem of distributed finite-time coordinated path-following for under-actuated autonomous surface vehicles (ASVs) within a network swarm. Each vehicle in swarm system suffers from velocity restrictions and multiple uncertainties including parameter perturbations and time-varying environment disturbances. Based on the constructed bionic swarm pattern and potential function, the swarm velocity guidance (SVG) with self-organization and collision avoidance is developed to guide ASV surge velocities and heading angles simultaneously. A distributed observer by adding correction terms to the vehicle model is involved to identify the lumped uncertainties, and the estimations are utilized as feed-forward compensation to weaken the uncertainty impact, thus achieving high tracking precision. By using asymmetric barrier Lyapunov function, the uncertainty observer based distributed surge and heading kinetics controllers under physical restrictions are devised to guarantee that the guided signals generated by SVG are tracked within finite time. Through simulation studies of swarm path-following, it is demonstrated that the designed control approach is feasible and efficient for multiple uncertain under-actuated ASVs.

Event-Triggered Dynamic Surface Control of an Underactuated Autonomous Surface Vehicle for Target Enclosing

This article addresses the event-triggered dynamic surface control of an underactuated autonomous surface vehicle with unknown kinetics for circumnavigating a dynamic target with unknown velocity. A modular design approach to the event-triggered dynamic surface control is proposed for target enclosing. In the estimator module, an extended state observer is employed for estimating the relative motion between the target and the surface vehicle. A fuzzy system is used for online modeling the unknown vehicle kinetics. In the controller module, an event-triggered dynamic surface control law is constructed by using the estimated relative velocities and vehicle kinetics in the estimator module. In the control law, a triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. Besides, the control inputs are bounded with the aid of a projection operator and saturated functions. The input-to-state stability of the closed-loop target enclosing system is proven through Lyapunov analysis. Simulation results substantiate the effectiveness of the event-triggered dynamic surface control method for circumnavigating a maneuvering target.

Neuro-adaptive trajectory tracking control of underactuated autonomous surface vehicles with high-gain observer

This paper addresses the trajectory tracking control of underactuated autonomous surface vehicles subject to immeasurable velocities and parameter uncertainties. An adaptive control scheme is proposed based on backstepping method, neural network and low-frequency techniques. In addition, the overall signals of closed-loop system are ensured uniformly ultimate boundness based on Lyapunov stability theory. The advantages are highlighted as follows: (i) To avoid explosion of complexity of conventional backstepping method, the derivations of virtual control signals are obtained through second-order tracking differentiator. (ii) The proposed controller does not require any previous knowledge about hydrodynamic damping and external disturbances, which is easily applied into practice. (iii) To solve the problem of immeasurable velocities, the controller does not acquire velocity signals by employing high-gain observer. Finally, simulation results are provided to verify strong robustness and tracking effectiveness of proposed control scheme

Neural network adaptive position tracking control of underactuated autonomous surface vehicle

The present study investigates the position tracking control of the underactuated autonomous surface vehicle, which is subjected to parameters uncertainties and external disturbances. In this regard, the backstepping method, neural network, dynamic surface control and the sliding mode method are employed to design an adaptive robust controller. Moreover, a Lyapunov synthesis is utilized to verify the stability of the closed-loop control system. Following innovations are highlighted in this study: (i) The derivatives of the virtual control signals are obtained through the dynamic surface control, which overcomes the computational complexities of the conventional backstepping method. (ii) The designed controller can be easily applied in practical applications with no requirement to employ the neural network and state predictors to obtain model parameters. (iii) The prediction errors are combined with position tracking errors to construct the neural network updating laws, which improves the adaptation and the tracking performance. The simulation results demonstrate the effectiveness of the proposed position tracking controller.