Control Of Unmanned Surface Vehicle Research Articles

Motion Control System for USV Target Point Convergence.

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).

Distributed deadband event-triggered observer-based containment control for unmanned surface vehicles with channel fading

This article investigates the distributed deadband event-triggered containment control problem for multiple underactuated surface vehicles (USV) systems whose communication networks are subject to channel fading. The first distributed deadband event-triggered observer (DDETO) was proposed to observe the target positions of the followers in the convex hulls formed by the leaders. It also manages the internal communication between the followers in the presence of channel fading. In addition, the deadband event-triggered mechanism (DETM) in DDETO guarantees a strictly positive minimum inter-event times (MIET), which can be accurately calculated and adjusted according to the design parameters. Afterward, an adaptive controller is designed by backstepping based on the desired position information observed by DDETO. It is worth noting that the containment control scheme proposed in this paper is based on relative position measurement only, which reduces the amount of information transfer compared to transmitting position and velocity information when wireless signals are fading in the channel, improves the control accuracy, and also reduces the system’s requirements for sensors. Theoretical analysis leads to the conclusion that the error signals are uniformly ultimately bounded (UUB). Finally, the effectiveness of the control scheme is verified by numerical simulation.

Event-triggered model-free adaptive sliding-mode heading control for unmanned surface vehicles under DoS attacks

This article investigates event-triggered model-free adaptive sliding-mode heading control issues for unmanned surface vehicles with dual-channel denial of service (DoS) attacks and bounded disturbances. Initially, we establish a redefined output. Meanwhile, we establish a virtual linear data model based on the redefined output and pseudo-partial-derivative technology. In addition, to reduce the effects of the external disturbance, we introduce the sliding mode control method to enhance the system’s robustness against disturbances. Then, an event-triggered communication scheme is developed to reduce the waste of communication resources. Two compensation schemes are formulated to eliminate the effects of dual-channel DoS attacks. So, the main contributions of the presented method are the enhancement of accuracy, reduction of computational burden, and increased robustness, all of which are demonstrated in the simulation section.

Trajectory tracking control of unmanned surface vehicle based on optimized barrier Lyapunov function under real ocean wave modeling

The state-constrained trajectory tracking control problem of unmanned surface vehicle (USV) under real wave interference is studied in this paper. This method is closer to the actual situation. Firstly, the interference of real ocean waves measured by ocean wave experiments on USV is considered. The wave spectrum and the equal interval method are used to simulate ocean waves in combination with the classical long-crested wave model. Secondly, the simulated wave is innovatively transformed into wave interference force, and the interference of the real wave is applied to the state constraint control of USV. The disturbance observer is used to observe and compensate for the external real wave interference, and the input saturation problem in real situations is fully considered. Furthermore, this paper proposes an improved logarithmic constraint function, which pre-set the performance function to constrain the trajectory tracking position error of the USV within a reasonable range. Finally, the rationality and effectiveness of the control strategy are verified by simulation.

Reinforcement learning-based distributed cooperative sliding mode control for unmanned surface vehicles

Reinforcement learning-based distributed cooperative sliding mode control for unmanned surface vehicles

Periodic event-triggered adaptive neural control of USVs under replay attacks

This work aims to address the control issue of unmanned surface vehicles (USVs) under replay attacks, where the influence of internal/external uncertainties and actuator’s physical constraints are taken into account. In the control design, a hyperbolic tangent function is used to replace the actuator saturation nonlinearity. To solve the design problem of mismatched unknown time-varying control gain caused by replay attacks, the single-parameter-learning technique is introduced, which avoids the operation of estimating for the unknown gain directly. To compensate for the effect of lumped uncertainties including attacks, unknown dynamics and external disturbances, the adaptive neural network with the finite covering principle is involved in the kinematic and dynamic channels, respectively. Furthermore, to reduce the update rate of actuator and alleviate the actuator wear, a periodic event-triggering mechanism (PETM) is established in the controller-actuator (C-A) channel. Finally, a periodic event-triggered (PET) adaptive neural tracking control solution for USVs under replay attacks is proposed, and it is verified that the control solution can force the trajectory of USVs to follow the reference trajectory even if there exists the effect of replay attacks. In addition, all signals in the closed-loop control system of USVs under replay attacks are bounded, and simulation and comparison results demonstrate the effectiveness of the control strategy.

Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

SummaryThis article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

[formula omitted] output tracking control for polynomial parameter-varying systems via homogeneous Lyapunov methods

This paper investigates the H∞ output tracking control for polynomial parameter-varying systems—a class of nonlinear parameter-varying systems. Based on the homogeneous polynomial Lyapunov function (HPLF), a state feedback controller is presented to make the augmented system (composed of the original system and the tracking error system) robust exponentially stable, so that the output of the original system can track the reference signal and satisfy the H∞ output tracking performance. The conservativeness of the theoretical results is reduced due to the following facts: (1) the HPLF can depend on the full states and time-varying parameters; and (2) there are no constraints on the input coefficient matrix and the inverse of the Lyapunov matrix. In particular, when the system states and time-varying parameters are confined to a compact set, local results are obtained based on the generalized S-procedure. The solvable conditions are given in terms of state-and-parameter-dependent linear matrix inequalities, which can be solved by sum of squares (SOS) techniques. Finally, the feasibility and effectiveness of the proposed method are verified by a numerical example and the heading control of unmanned surface vehicle.

Active disturbance rejection heading control of USV based on parameter tuning via an improved pigeon-inspired optimization

An improved active disturbance rejection control (ADRC) algorithm is proposed in this paper to enhance the heading control capabilities of unmanned surface vehicles (USVs) under wind and wave disturbances. The algorithm introduces two enhancements: parameter tuning and fitting, alongside the optimization of the nonlinear function in the ADRC algorithm. First, the parameter tuning employs an improved pigeon-inspired optimization (PIO) algorithm, which encompasses two strategies: the adaptive strategy and the wandering strategy. Parameter fitting ensures discretely optimized value transition into a continuous state, allowing dynamic parameter adjustments. Second, the optimization of the nonlinear function uses the D-value fitting method. Overall, the improved ADRC algorithm significantly enhances response speed to heading control commands for USVs, fortifying their resistance against wind and wave disturbances. Our proposed algorithm provides a new approach to achieve precise USV heading control.

Predefined-Time Tracking Control of Unmanned Surface Vehicle under Complex Time-Varying Disturbances

Aiming at the unmanned surface vehicle (USV) trajectory tracking control under complex time-varying environment, a predefined-time convergence sliding mode disturbance observer (PTC-SMO) is introduced to effectively handle the internal parameter uncertainties and external environmental disturbances, thereby guaranteeing precise compensation of the lumped disturbance term within a set time. Then, in order to achieve precise tracking of the desired trajectory using USV under a predetermined time constraint, a novel fast trajectory tracking control strategy with predefined-time convergence (PTC-FTTCS) is established to improve tracking performance and ensure that the trajectory tracking error converges quickly in the predefined time. Through rigorous comparative simulation under ideal conditions and time-varying disturbances, the results demonstrate reliable trajectory tracking and disturbance handling effects, and the tracking performance and disturbance observation performance are significantly better than state-of-the-art methods.

Robust finite-time sliding mode control of unmanned surface vehicle with active compensation of pose estimation uncertainty

Robust control of Unmanned Surface Vehicles (USVs) is crucial for their safe and reliable navigation. The control methods of USV generally regard the results of pose estimation (such as GNSS/INS) as an accurate reference, but GNSS/INS is usually fluctuating and uncertain in the open water, which easily leads to large tracking errors or even control failure of USV. To this end, a robust sliding mode control of USV considering the active compensation of pose estimation uncertainty is innovatively presented, which can ensure the stability and accuracy of USV's control. The distinctive features of the proposed method are twofold: (i) To improve the pose estimation accuracy and obtain accurate uncertainty evaluation, an improved adaptive Kalman filtering considering the time-varying solution state is presented, which utilizes the convergence characteristics of INS and the time-varying features of GNSS solution state; (ii) By integrating pose estimation uncertainty assessment and active compensation, a Robust Finite-Time Sliding Mode Control (RFT-SMC) for a twin-propeller unmanned surface vehicle is developed to enhance tracking accuracy and anti-disturbance capability, and its convergence is established using a Lyapunov function, ensuring reliable operation of the USV even in the presence of uncertain pose estimation. Finally, verification experiments are offered to verify the effectiveness of the presented RFT-SMC method.

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.

Event-Based Distributed Secure Control of Unmanned Surface Vehicles With DoS Attacks

Event-Based Distributed Secure Control of Unmanned Surface Vehicles With DoS Attacks

Policy Learning for Nonlinear Model Predictive Control With Application to USVs

The unaffordable computation load of nonlinear model predictive control (NMPC) has prevented it for being used in robots with high sampling rates for decades. This paper is concerned with the policy learning problem for nonlinear MPC with system constraints, where the nonlinear MPC policy is learned offline and deployed online to resolve the computational complexity issue. A deep neural networks (DNN) based policy learning MPC (PL-MPC) method is proposed to avoid solving nonlinear optimal control problems online. The detailed policy learning method is developed and the PL-MPC algorithm is designed. The strategy to ensure the practical feasibility of policy implementation is proposed, and it is theoretically proved that the closed-loop system under the proposed method is asymptotically stable in probability. In addition, we apply the PL-MPC algorithm successfully to the motion control of unmanned surface vehicles (USVs). It is shown that the proposed algorithm can be implemented at a sampling rate up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm{5}~Hz$</tex-math></inline-formula> with high-precision motion control.

Fixed-time adaptive neural quantized formation control of USVs with tunnel prescribed performance

This paper investigates fixed-time adaptive neural quantized formation control of unmanned surface vehicles (USVs) with tunnel prescribed performance in the presence of leader’s information unknown, model dynamic uncertainties and input quantization. Initially, a fixed-time observer is constructed to rapidly estimate the leader’s unknown information. Following this, a control strategy is designed employing a fixed-time sliding mode differentiator-based backstepping technique combined with a neural network-based minimum parameter learning approach (MLP-NN). This strategy addresses the ”complexity explosion” issue arising from the differentiation of the virtual control law and model dynamic uncertainties separately. Moreover, a specialized prescribed performance function is incorporated to ensure that performance specifications of the formation tracking errors meet specific performance criteria. The integration of hysteresis quantizer aids in conserving save communication resources and significantly reduces the detrimental effects caused by quantization errors through the proposed control methodology. It is demonstrated that all closed-loop signals are practical fixed-time stable and formation tracking errors consistently adhere to the predetermined performance envelopes. The efficacy of proposed control strategy is validated through comprehensive numerical simulations.

Adaptive neural synergetic heading control for USVs with unknown dynamics and disturbances

This paper delves into the challenge of heading control for unmanned surface vehicles (USVs) in conditions with unknown dynamics and environmental disturbances. Adaptive neural synergetic controllers are introduced for the purpose of effectively steering the USV, ensuring precise heading control to track and maintain the desired angle, even in the presence of environmental disturbances such as sea winds and waves. Firstly, different forms of macro variables and evaluation constraints are introduced to design the synergetic controllers. Then, to estimate the unknown dynamics and environmental disturbances, a single input single output RBF neural network is developed. Besides, to ensure tracking at the desired angle, an adaptive neural synergetic law is derived from the Lyapunov stability theorem. Finally, the simulation results compare the control effect of heading controllers with different macro variables and evaluation constraints and verify the effectiveness of the proposed control strategy. The feasibility of the adaptive neural synergetic controller is verified through the USV experiment.

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Path Following Control for Unmanned Surface Vehicles: A Reinforcement Learning-Based Method With Experimental Validation.

In this article, a reinforcement learning (RL)-based strategy for unmanned surface vehicle (USV) path following control is developed. The proposed method learns integrated guidance and heading control policy, which directly maps the USV's navigation states to motor control commands. By introducing a twin-critic design and an integral compensator to the conventional deep deterministic policy gradient (DDPG) algorithm, the tracking accuracy and robustness of the controller can be significantly improved. Moreover, a pretrained neural network-based USV model is built to help the learning algorithm efficiently deal with unknown nonlinear dynamics. The self-learning and path following capabilities of the proposed method were validated in both simulations and real sea experiments. The results show that our control policy can achieve better performance than a traditional cascade control policy and a DDPG-based control policy.

An Anti-Congestion Interaction Mechanism Based Leaderless Formation Control for Unmanned Surface Vehicles With Communication Interruptions

An Anti-Congestion Interaction Mechanism Based Leaderless Formation Control for Unmanned Surface Vehicles With Communication Interruptions

Distributed Optimal Coordinated Control for Unmanned Surface Vehicles with Interleaved Periodic Event-based Mechanism

Distributed Optimal Coordinated Control for Unmanned Surface Vehicles with Interleaved Periodic Event-based Mechanism