Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties

The paper presents a nonlinear control law for a marine vessel to track a reference trajectory. In the wake of theresults obtained in [19], an integrative approach is incorporated in the linear algebra methodology in order toreduce the effect of the uncertainty in the tracking error. This new approach does not increase the complexityof the design methodology. In addition, the zero convergence of tracking error under polynomial uncertaintiesis demonstrated. Simulation results under environmental disturbance and model mismatches are presentedand discussed.Fil: Serrano, Mario Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Godoy Bordes, Sebastian Alejandro. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Gandolfo, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentin

Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered

An environmental disturbance observer framework for autonomous surface vessels

This paper proposes a robust disturbance observer framework for maritime autonomous surface vessels considering model and measurement uncertainties. The core contribution lies in a nonlinear disturbance observer, reconstructing the forces on a vessel impacted by the environment. For this purpose, mappings are found leading to synchronized global exponentially stable error dynamics. With the stability theory of Lyapunov, it is proven that the error converges exponentially into a ball, even if the disturbances are highly dynamic. Since measurements are affected by noise and physical models can be erroneous, an unscented Kalman filter (UKF) is used to generate more reliable state estimations. In addition, a noise estimator is introduced, which approximates the noise strength. Depending on the severity of the measurement noise, the observed disturbances are filtered through a cascaded structure consisting of a weighted moving average (WMA) filter, a UKF, and the proposed disturbance observer. To investigate the capability of this observer framework, the environmental disturbances are simulated dynamically under consideration of different model and measurement uncertainties. It can be seen that the observer framework can approximate dynamical forces on a vessel impacted by the environment despite using a low measurement sampling rate, an erroneous model, and noisy measurements.publishedVersio

Review of sliding mode control application in autonomous underwater vehicles

973-984This paper presents a review of sliding mode control for autonomous underwater vehicles (AUVs). The AUVs are used under water operating in the presence of uncertainties (due to hydrodynamics coefficients) and external disturbances (due to water currents, waves, etc.). Sliding mode controller is one of the nonlinear robust controllers which is robust towards uncertainties, parameter variations and external disturbances. The evolution of sliding mode control in motion control studies of autonomous underwater vehicles is summarized throughout for the last three decades. The performance of the controller is examined based on the chattering reduction, accuracy (steady state error reduction), and robustness against perturbation. The review on sliding mode control for AUVs provides insights for readers to design new techniques and algorithms, to enhance the existing family of sliding mode control strategies into a new one or to merge and re-supervise the control techniques with other control strategies, in which, the aim is to obtain good controller design for AUVs in terms of great performance, stability and robustness

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles

This work proposes a nonlinear model predictive control-based guidance strategy for unmanned surface vehicles, focused on path following. The application of this strategy, in addition to overcome drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for tracking the references provided by the guidance strategy. The stability and robustness of the proposed strategy are theoretically discussed. Furthermore, given the non-negligible computational cost of such nonlinear predictive guidance strategy, a practical nonlinear model predictive control strategy is also applied in order to reduce the computational cost to a great extent. The effectiveness and advantages of both proposed strategies over other nonlinear guidance laws are illustrated through a complete set of simulations.Comment: 21 pages, 15 figures. Postprint of the final published wor

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Spatio-Temporal Deep Learning Approaches for Addressing Track Association Problem using Automatic Identification System (AIS) Data

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets

Mixed control for trajectory tracking in marine vessels

[EN] This work proposes the design of an adaptive controller for a marine vessel; the proposed control strategy applies a controller designed on linear algebra for the kinematics and an adaptive control technique for the dynamic part of the vessel. The linear algebra based controller (LABC) for kinematics receives the desired position references and this generates another reference velocity pair for the adaptive (dynamic) controller. The main goal of the application of the adaptive control technique in this kind of enforcement is presented in the case that the mass of the vessel varies with its trajectory (e.g. fishing vessel, refueling vessel, etc.) where the adaptive controller adjusts its parameters through of adaptation law, which in turn generates a control action that compensates dynamic variations of the ship. Besides, this work presents the stability analysis and adaptive adjustment law based on the Lyapunov theory. And the simulation results that are presented prove that the control can deal with non-linearities and time-variant dynamics.[ES] Este trabajo muestra el diseño de un controlador adaptable para un buque marino; la estrategia de control que se propone es la aplicación de un controlador basado en álgebra lineal para la cinemática y una técnica de control adaptable para la parte dinámica del buque. El controlador basado en álgebra lineal (LABC) para cinemática recibe las referencias de posición deseadas y esto genera otro par de velocidad de referencia para el controlador adaptable (dinámico). El objetivo principal de la aplicación de la técnica de control adaptable se presenta en el caso de que la masa del buque varíe con su trayectoria (por ejemplo, buque pesquero, buque de reabastecimiento de combustible, etc.) donde el controlador adaptable ajusta sus parámetros mediante la ley de adaptación, que a su vez genera una acción de control que compensa las variaciones dinámicas del buque. Además, este trabajo presenta el análisis de estabilidad y la ley de ajuste adaptable basada en la teoría de Lyapunov. Los resultados de simulación muestran que el sistema puede seguir las señales de referencia con un error muy bajo aún en presencia de incertidumbre.Vacca Sisterna, C.; Serrano, E.; Scaglia, G.; Rossomando, F. (2021). Control mixto para el seguimiento de trayectoria en buques marinos. Revista Iberoamericana de Automática e Informática industrial. 19(1):27-36. https://doi.org/10.4995/riai.2021.15027OJS2736191Cui R, Chen L, Yang C, Chen M. "Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities". IEEE Transactions on Industrial Electronics 2017; 64(8): 6785-6795. https://doi.org/10.1109/TIE.2017.2694410Dai SL, He S, Lin H. "Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles". 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IEEE Transactions on Control Systems Technology 2006; 14(6): 1150-1157. https://doi.org/10.1109/TCST.2006.880220He W, Yin Z, Sun C. "Adaptive neural network control of a marine vessel with constraints using the asymmetric barrier Lyapunov function".IEEE transactions on cybernetics 2016; 47(7): 1641-1651. https://doi.org/10.1109/TCYB.2016.2554621Hu X, Du J, Zhu G, Sun Y. "Robust adaptive NN control of dynamically positioned vessels under input constraints". Neurocomputing 2018; 318: 201-212. https://doi.org/10.1016/j.neucom.2018.08.056Liao Yl, Wan L, Zhuang Jy. "Backstepping dynamical sliding mode control method for the path following of the underactuated surface vessel". Procedia Engineering 2011; 15: 256-263. https://doi.org/10.1016/j.proeng.2011.08.051Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., & BastosFilho, T. F. (2008). An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice, 16(11), 1354-1363. https://doi.org/10.1016/j.conengprac.2008.03.004Nie J, Lin X. "Robust Nonlinear Path Following Control of UnderactuatedMSV With Time-Varying Sideslip Compensation in the Presence of Actuator Saturation and Error Constraint". IEEE Access 2018; 6: 71906-71917. https://doi.org/10.1109/ACCESS.2018.2881513Scaglia, Gustavo; Serrano, Emanuel; Albertos, Pedro (2020). Control de Trayectorias Basado en Algebra Lineal. Revista Iberoamericana de Automática e Informática industrial, [S.l.], ago. 2020. ISSN 1697-7920. Disponible en: https://polipapers.upv.es/index.php/RIAI/article/view/13584. https://doi.org/10.4995/riai.2020.13584Scaglia Gustavo, Serrano Mario Emanuel, Albertos Pedro (2020). "Linear Algebra Based Controller - Design and Applications". Publisher: Springer International Publishing. eBook ISBN 978-3-030-42818-1. Hardcover ISBN 978-3-030-42817-4. DOI 10.1007/978-3-030-42818-1.Scaglia, G., Mut, V., Rosales, A., Quintero, O., "Tracking Control of a Mobile Robot using Linear Interpolation", Proceeding of the 3rd International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2007. vol. 1, pp. 11-15, ISBN: 978-2-9520712-7-7 February 8-10, 2007Serrano M.E., Scaglia G.J.E., Auat Cheein F., Mut V. and Ortiz O.A. (2015).Trajectory-tracking controller design with constraints in the control signals: a case study in mobile robots. Robotica, 33, pp 2186-2203, diciembre 2015. https://doi.org/10.1017/S0263574714001325Serrano ME, Godoy SA, Gandolfo D, Mut V, Scaglia G. "Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties". Information Technology And Control 2018; 47(1): 118-130. https://doi.org/10.5755/j01.itc.47.1.17782Tee KP, Ge SS. "Control of fully actuated ocean surface vessels using a class of feedforward approximators". 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Robust Course Keeping Control of a Fully Submerged Hydrofoil Vessel with Actuator Dynamics: A Singular Perturbation Approach

This paper presents a two-time scale control structure for the course keeping of an advanced marine surface vehicle, namely, the fully submerged hydrofoil vessel. The mathematical model of course keeping control for the fully submerged hydrofoil vessel is firstly analyzed. The dynamics of the hydrofoil servo system is considered during control design. A two-time scale model is established so that the controllers of the fast and slow subsystems can be designed separately. A robust integral of the sign of the error (RISE) feedback control is proposed for the slow varying system and a disturbance observer based state feedback control is established for the fast varying system, which guarantees the disturbance rejection performance for the two-time scale systems. Asymptotic stability is achieved for the overall closed-loop system based on Lyapunov stability theory. Simulation results show the effectiveness and robustness of the proposed methodology