Control Of Autonomous Surface Vehicle Research Articles

Time-Varying Formation Control of Autonomous Surface Vehicles Based on Affine Observer

Time-Varying Formation Control of Autonomous Surface Vehicles Based on Affine Observer

Neural network-based practical fixed-time nonsingular sliding mode tracking control of autonomous surface vehicles under actuator saturation

The article considers the fixed-time tracking control of autonomous surface vehicles (ASVs) subject to actuator saturation. A novel sliding variable with state-dependent exponents is first constructed to alleviate chattering and overcome the singularity. By using the proposed fixed-time sliding variable and the neural network (NN), an NN-based fixed-time sliding mode control (SMC) scheme is developed. Besides, an auxiliary system is utilized to solve the input saturation constraint. Unlike some existing results for ASVs with input saturation, the designed fixed-time SMC law does not require assumptions on a priori upper bound of the lumped disturbances. Through Lyapunov analysis, it is proven that the tracking error can converge to a bounded region in a fixed time. Simulation examples are given to show the validity of the proposed control scheme.

Fully Distributed Target Encircling Control of Autonomous Surface Vehicles Based on Noncooperative Games

Fully Distributed Target Encircling Control of Autonomous Surface Vehicles Based on Noncooperative Games

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

State Recovery and Fault-tolerant Control of Autonomous Surface Vehicle with Actuator and Sensor Faults

State Recovery and Fault-tolerant Control of Autonomous Surface Vehicle with Actuator and Sensor Faults

Safety-certified Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

Safety-certified Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

Online learning-based active disturbance rejection control of autonomous surface vehicles with HIL simulations

Modeling and control of autonomous surface vehicles (ASVs) is a challenging task due to the requirement of real-time adaptation to internal hydrodynamic parameter variations and external ocean disturbances induced by wind, waves, and ocean currents. In this paper, an online learning-based active disturbance rejection control method is proposed by combining real-time model learning and anti-disturbance control. Specifically, a filtering learning extended state observer (FLESO) is proposed for online identifying the unknown hydrodynamic parameters, the unknown control input gain, and estimating system uncertainties composed of the unknown external disturbances and dynamic modeling errors, with guaranteed convergence. Then, a self-learning anti-disturbance control law is developed based on the FLESO, enabling self-learning anti-disturbance speed tracking without prior knowledge of model parameters. A salient feature of the proposed method is that the unknown hydrodynamic parameters, the unknown control input gain, and the unknown system uncertainties can be estimated online simultaneously. Lyapunov stability analysis is employed to verify the stability of the closed-loop system. The effectiveness of the proposed online learning-based active disturbance rejection control method is demonstrated through HIL simulations.

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

This paper proposes a fixed-time adaptive path-following control scheme for autonomous surface vehicles (ASVs) to address accuracy and safety considerations in actual navigation practice. The proposed scheme is designed to effectively follow a reference path while satisfying output performance and feasibility constraints during navigation. For the output constraint problem, a modified version of the universal barrier Lyapunov function is used for the analysis of path-dependent constraint requirements. Additionally, a new type of path-dependent constraint function is proposed that depends on the path parameters rather than directly on the time variables. A new adaptive backstepping method is also devised to address the problem of fixed-time convergence, as well as the 'complexity explosion' in the controller design process, and the unknown dynamics of the model. The study shows that, under this control scheme, the ASV's position and heading errors can converge to a set near the equilibrium point at a fixed time convergence rate, while always satisfying the path-dependent constraints. Simulation and experimental studies further validate the effectiveness of the proposed scheme.

Robust adaptive event-triggered path following control for autonomous surface vehicles in shallow waters

This paper utilizes an adaptive event-triggered mechanism to investigate the path following control of autonomous surface vehicles (ASVs) in shallow waters. Given the challenges posed by the shallow water environment to ASV maneuverability and heading stability, the controller design must enhance the robustness and reduce model complexity. Firstly, a time-varying drift angle-based course guidance law was proposed using a position error dynamics model and a reduced-order extended state observer. Secondly, to simplify the controller's design steps, the ASV's surge and sway motion models are decoupled and transformed. Then, an event-triggered mechanism controller was designed to activate the ASV's rudder angle and shaft speed. In addition, a new robust term was introduced to further estimate the residuals of the RBF neural network, the external disturbances and the event-triggered residual term, thus eliminating the requirement for an upper bound on the unknown disturbances and residuals. The proposed control method based on the robust adaptive event-triggered mechanism can guarantee that all estimation and tracking errors are uniformly ultimately bounded. Simulation results demonstrate that the controller can ensure effective path following of vessels in shallow waters.

Distributed Affine Formation Maneuver Control of Autonomous Surface Vehicles With Event-Triggered Data Transmission Mechanism

This article investigates the distributed affine formation maneuver control for collaborative autonomous surface vehicles (ASVs) systems with the event-triggered data transmission mechanism among inter-ASVs. To achieve preset arbitrary configuration transformations in the case where the formation knowledge is assigned to only a part of ASVs, the affine image is employed to describe desired configurations. To ensure position and attitude configuration rigidities simultaneously, a synthetical constraint composed of the Laplacian matrices and the stress matrices is constructed. Besides, an algebraic relation between the synthetical constraint and the formation tracking error is established. The above tools enable the sliding mode technology to be used to the affine formation maneuver control of collaborative ASVs systems. It follows that a novel distributed event-triggered affine formation maneuver control scheme is developed for the collaborative ASVs systems. The advantages of the proposed control scheme are threefold: 1) the distributed affine formation maneuver control of collaborative second-order vehicles systems is achieved with changeable formation velocities and orientations; 2) the existing literatures adapt to developing the second-order distributed affine formation maneuver control based on the backstepping philosophy. In contrast, the sliding manifold replaces the backstepping philosophy here. Since the complexity explosion is circumvented, the proposed control scheme is relatively simple; and 3) the event-triggered data transmission mechanism among inter-ASVs is introduced, and neighbor velocities and accelerations are omitted for the control implementation. As a consequence, the communication resource usage can be reduced. Finally, the semi-physical simulations are conducted to illustrate the effectiveness of the proposed control scheme.

Adaptive Fuzzy Control of Autonomous Surface Vehicles for Fast Accurate Trajectory Tracking

Adaptive Fuzzy Control of Autonomous Surface Vehicles for Fast Accurate Trajectory Tracking

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.

Adaptive anti‐disturbance sampling control of autonomous surface vehicles based on discrete‐time concurrent learning extended state observers

SummaryThis paper investigates the surge speed tracking control of an autonomous surface vehicle (ASV) subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. An adaptive anti‐disturbance sampling control method is proposed for an ASV without using any model parameters. Specifically, by utilizing real‐time and historical data, discrete‐time reduced‐order and full‐order concurrent learning extended state observers (CLESOs) are designed to estimate the unknown ASV model parameters and ensure estimation convergence without requiring persistent excitation. Then, a surge speed tracking control law is designed based on the discrete‐time full‐order CLESO. Through Lyapunov stability analysis, the closed‐loop system is proven to be stable, and the estimation and tracking errors are bounded. Simulation and hardware‐in‐loop experimental results validate the effectiveness of the proposed discrete‐time CLESOs for the surge speed tracking of an ASV with fully unknown dynamic model.

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.

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

This paper studies a time-varying formation control problem of a swarm of autonomous surface vehicles (ASVs) with prescribed constraints. Every ASV suffers from unavailable velocities, saturated inputs, internal uncertainties, and external disturbances. A distributed finite-time performance-prescribed time-varying formation control method is proposed for ASVs. Specifically, an adaptive fuzzy state observer (AFSO) is firstly proposed to recover the unavailable velocity by using saturated inputs and estimated disturbances from the fuzzy logic system. Secondly, a tunnel prescribed performance (TPP) function is designed to limit tracking errors with a concise form and smaller overshoot. At the kinematic level, with TPP-based error transformation, a C1 finite-time time-varying guidance law is proposed by using the smooth switch functions and recovered velocities. Next, a finite-time command filter based on Levant differentiator is presented to smooth the guidance signals. At the kinetic level, a C1 finite-time saturated control law is developed with the estimated velocities and disturbances. Then, it proves that tracking errors of the proposed closed-loop system converge into a small neighborhood around the equilibrium within finite time while evolving under TPP constraints regardless of saturated inputs. Finally, comparison results are provided to demonstrate the effectiveness of the proposed distributed finite-time performance-prescribed time-varying formation control method.

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

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.

Neural network based adaptive sliding mode tracking control of autonomous surface vehicles with input quantization and saturation

The autonomous surface vehicle (ASV) is supposed to be able to adapt unknown environments without human interference. This paper investigates the problem of trajectory tracking control for ASVs in the present of parameter uncertainties, disturbances, input quantization and actuator saturation. The neural network (NN) approximation and an adaptive sliding mode control (SMC) strategy are combined for the ASVs with a priori unknown upper bound of parameter uncertainties, external disturbances and saturation errors. The NN is adopted to approximate the unknown continuous uncertainties and disturbances in the system. The adaptive law for the SMC parameter can guarantee no overestimation of the robust control parameter with respect to unknown upper bounds of the NN approximation errors and the saturation error. By adding the NN approximation into the adaptive SMC laws, the robust control parameter and the chattering can be reduced. Uniformly ultimately bounded (UUB) for sliding variables and tracking errors can be guaranteed by Lyapunov proof. Simulation examples are given to illustrate the improved performance with the proposed approach. The comparisons on the proposed control method with other algorithms are given by using the integrated absolute error (IAE) and integrated time absolute error (ITAE).

Time-Varying Nonsingular Terminal Sliding Mode control of Autonomous Surface Vehicle with predefined convergence time

This paper addresses the problem of trajectory tracking control of an Autonomous Surface Vehicle (ASV) with the presence of uncertainties and disturbances. First, a Time-Varying Nonsingular Terminal Sliding Mode (TV-NTSM) manifold is designed by incorporating a piecewise-defined function of time into a nonsingular terminal sliding mode (NTSM) manifold. As a result, the reaching phase that exists in traditional sliding mode is totally suppressed as well as the avoidance of the singularity problem. Then, a TV-NTSM controller is proposed to guarantee the convergence of the tracking errors to the origin in finite-time and to ensure the global robustness of the system with respect to large uncertainties and unknown environmental disturbances. To meet the mission requirement, an adequate method is given for the parameters system selection to guarantees the convergence of the tracking errors to the origin in prespecified time. Furthermore, an adaptive tuning law is introduced to estimate the unknown upper bounds of the lumped uncertainty, which is convenient for practical implementation. The stability of the closed-loop system is verified by Lyapunov theory and simulation results are carried out to prove the effectiveness of the proposed control algorithm.

Multitask Cooperative Formation Control of Autonomous Surface Vehicles With Interception of Moving Objects

Multitask Cooperative Formation Control of Autonomous Surface Vehicles With Interception of Moving Objects

Development of a Supplementary Outboard Side Thruster System for Dynamic Positioning Control of Autonomous Surface Vehicle

Development of a Supplementary Outboard Side Thruster System for Dynamic Positioning Control of Autonomous Surface Vehicle