Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Design of UDE-based dynamic surface control for dynamic positioning of vessels with complex disturbances and input constraints

In practice, dynamic positioning (DP) vessels are subjected to complex disturbances as well as the magnitude and changing rate constraints of the thrusts and moments. This study applied a dynamic surface controller based on an uncertainty and disturbance estimator (UDE) to a DP vessel with complex disturbances and input constraints. The UDE was designed to estimate and handle the complex disturbances. An auxiliary dynamic system (ADS) and smooth switching function were employed to compensate for the input constraints and avoid the singularity phenomenon caused by the ADS, respectively. The combination of the UDE method and dynamic surface control (DSC) technology significantly simplified the design process for the control law and increased the practicability for DP vessels. The stability of the proposed control law was proved using the Lyapunov theory. The effectiveness of the control law and possibility of actually applying it to a DP vessel were verified using simulation experiments

A Study on the Automatic Ship Control Based on Adaptive Neural Networks

Recently, dynamic models of marine ships are often required to design advanced control systems. In practice, the dynamics of marine ships are highly nonlinear and are affected by highly nonlinear, uncertain external disturbances. This results in parametric and structural uncertainties in the dynamic model, and requires the need for advanced robust control techniques. There are two fundamental control approaches to consider the uncertainty in the dynamic model: robust control and adaptive control. The robust control approach consists of designing a controller with a fixed structure that yields an acceptable performance over the full range of process variations. On the other hand, the adaptive control approach is to design a controller that can adapt itself to the process uncertainties in such a way that adequate control performance is guaranteed. In adaptive control, one of the common assumptions is that the dynamic model is linearly parameterizable with a fixed dynamic structure. Based on this assumption, unknown or slowly varying parameters are found adaptively. However, structural uncertainty is not considered in the existing control techniques. To cope with the nonlinear and uncertain natures of the controlled ships, an adaptive neural network (NN) control technique is developed in this thesis. The developed neural network controller (NNC) is based on the adaptive neural network by adaptive interaction (ANNAI). To enhance the adaptability of the NNC, an algorithm for automatic selection of its parameters at every control cycle is introduced. The proposed ANNAI controller is then modified and applied to some ship control problems. Firstly, an ANNAI-based heading control system for ship is proposed. The performance of the ANNAI-based heading control system in course-keeping and turning control is simulated on a mathematical ship model using computer. For comparison, a NN heading control system using conventional backpropagation (BP) training methods is also designed and simulated in similar situations. The improvements of ANNAI-based heading control system compared to the conventional BP one are discussed. Secondly, an adaptive ANNAI-based track control system for ship is developed by upgrading the proposed ANNAI controller and combining with Line-of-Sight (LOS) guidance algorithm. The off-track distance from ship position to the intended track is included in learning process of the ANNAI controller. This modification results in an adaptive NN track control system which can adapt with the unpredictable change of external disturbances. The performance of the ANNAI-based track control system is then demonstrated by computer simulations under the influence of external disturbances. Thirdly, another application of the ANNAI controller is presented. The ANNAI controller is modified to control ship heading and speed in low-speed maneuvering of ship. Being combined with a proposed berthing guidance algorithm, the ANNAI controller becomes an automatic berthing control system. The computer simulations using model of a container ship are carried out and shows good performance. Lastly, a hybrid neural adaptive controller which is independent of the exact mathematical model of ship is designed for dynamic positioning (DP) control. The ANNAI controllers are used in parallel with a conventional proportional-derivative (PD) controller to adaptively compensate for the environmental effects and minimize positioning as well as tracking error. The control law is simulated on a multi-purpose supply ship. The results are found to be encouraging and show the potential advantages of the neural-control scheme.1. Introduction = 1 1.1 Background and Motivations = 1 1.1.1 The History of Automatic Ship Control = 1 1.1.2 The Intelligent Control Systems = 2 1.2 Objectives and Summaries = 6 1.3 Original Distributions and Major Achievements = 7 1.4 Thesis Organization = 8 2. Adaptive Neural Network by Adaptive Interaction = 9 2.1 Introduction = 9 2.2 Adaptive Neural Network by Adaptive Interaction = 11 2.2.1 Direct Neural Network Control Applications = 11 2.2.2 Description of the ANNAI Controller = 13 2.3 Training Method of the ANNAI Controller = 17 2.3.1 Intensive BP Training = 17 2.3.2 Moderate BP Training = 17 2.3.3 Training Method of the ANNAI Controller = 18 3. ANNAI-based Heading Control System = 21 3.1 Introduction = 21 3.2 Heading Control System = 22 3.3 Simulation Results = 26 3.3.1 Fixed Values of n and = 28 3.3.2 With adaptation of n and r = 33 3.4 Conclusion = 39 4. ANNAI-based Track Control System = 41 4.1 Introduction = 41 4.2 Track Control System = 42 4.3 Simulation Results = 48 4.3.1 Modules for Guidance using MATLAB = 48 4.3.2 M-Maps Toolbox for MATLAB = 49 4.3.3 Ship Model = 50 4.3.4 External Disturbances and Noise = 50 4.3.5 Simulation Results = 51 4.4 Conclusion = 55 5. ANNAI-based Berthing Control System = 57 5.1 Introduction = 57 5.2 Berthing Control System = 58 5.2.1 Control of Ship Heading = 59 5.2.2 Control of Ship Speed = 61 5.2.3 Berthing Guidance Algorithm = 63 5.3 Simulation Results = 66 5.3.1 Simulation Setup = 66 5.3.2 Simulation Results and Discussions = 67 5.4 Conclusion = 79 6. ANNAI-based Dynamic Positioning System = 80 6.1 Introduction = 80 6.2 Dynamic Positioning System = 81 6.2.1 Station-keeping Control = 82 6.2.2 Low-speed Maneuvering Control = 86 6.3 Simulation Results = 88 6.3.1 Station-keeping = 89 6.3.2 Low-speed Maneuvering = 92 6.4 Conclusion = 98 7. Conclusions and Recommendations = 100 7.1 Conclusion = 100 7.1.1 ANNAI Controller = 100 7.1.2 Heading Control System = 101 7.1.3 Track Control System = 101 7.1.4 Berthing Control System = 102 7.1.5 Dynamic Positioning System = 102 7.2 Recommendations for Future Research = 103 References = 104 Appendixes A = 112 Appendixes B = 11

Mixed control for trajectory tracking in marine vessels

Este trabajo muestra la estrategia de control de un controlador basado en álgebra lineal para la cinemática y una técnica de control adaptable para la parte dinámica del buque. Que en el primer caso (LABC) es aplicado sobre la cinemática que recibe las referencias de posición deseadas y esto genera otro par de velocidad de referencia para el controlador adaptable (dinámico). El objetivo principal de esta técnica de control combinada (LABC-adaptable) se presenta en el caso de que la masa del buque (u otro parámetro) varíe con su trayectoria (por ejemplo, buque pesquero, buque de reabastecimiento de combustible, etc.) donde el controlador combinado con características adaptables ajusta sus parámetros mediante una ley de sintonía, 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 LABC-adaptable basada en el criterio de estabilidad de Lyapunov. Los resultados obtenidos por simulación demuestran que el sistema marino puede seguir las señales de referencia con pequeños errores aún en presencia de incertidumbres.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 nonlinearities and time-variant dynamics.Fil: Vacca Sisterna, Carlos Alberto. 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: Serrano, Mario Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Rossomando, Francisco Guido. 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; Argentin

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. 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A Hybrid Nonlinear Model Predictive Control and Recurrent Neural Networks for Fault-Tolerant Control of an Autonomous Underwater Vehicle

The operation of Autonomous Unmanned Vehicles (AUVs) that is used for environment protection, risk evaluation and plan determination for emergency, are among the most important and challenging problems. An area that has received much attention for use of AUVs is in underwater applications where much work remains to be done to equip AUVs with systems to steer them accurately and reliably in harsh marine environments. Design of control strategies for AUVs is very challenging as compared to other systems due to their operational environment (ocean). Particularly when hydrodynamic parameters uncertainties are to be integrated into both the controller design as well as AUVs nonlinear dynamics. On the other hand, AUVs like all other mechanical systems are prone to faults. Dealing effectively with faulty situations for mechanical systems is an important consideration since faults can result in abnormal operation or even a failure. Hence, fault tolerant and fault-accommodating methods in the controller design are among active research topics for maintaining the reliability of complex AUV control systems. The objective of this thesis is to develop a nonlinear Model Predictive Control (MPC) that requires solving an online Quadratic Programming (QP) problem by using a Recurrent Neural Network (RNN). Also, an Extended Kalman Filter (EKF) is integrated with the developed scheme to provide the MPC algorithm with the system states estimates as well as a nonlinear prediction. This hybrid control approach utilizes both the mathematical model of the system as well as the adaptive nature of the intelligent technique through neural networks. The reason behind the selection of MPC is to benefit from its main capability in optimization within the current time slots while taking future time slots into consideration. The proposed control method is integrated with EKF which is an appropriate method for state estimation and data reconciliation of nonlinear systems. In order to address the high performance runtime cost of solving the MPC problem (formulated as a quadratic programming problem), an RNN is developed that has a low model complexity as well as good performance in real-time implementation. The proposed method is first developed to control an AUV following a desired trajectory. Since the problem of trajectory tracking and path following of AUVs exhibit nonlinear behavior, the effectiveness of the developed MPC-RNN algorithm is studied in comparison with two other control system methods, namely the linear MPC using Kalman Filter (KF) and the conventional nonlinear MPC using the EKF. In order to guarantee the fault-tolerant features of our proposed control method when faced with severe actuator faults, the developed MPC-RNN scheme is integrated with a dual Extended Kalman Filter that is used for a combined estimation of AUV states and parameters. The actuator faults are defined as the system parameters that are to be estimated online by the dual-EKF. Therefore, the developed Active Fault-Tolerant Control (AFTC) strategy is then applied to an AUV faced with loss of effectiveness (LOE) actuator fault scenarios while following a trajectory. Analysis and discussions regarding the comparison of the proposed AFTC method with Fault-Tolerant Nonlinear Model Predictive Control (FTNMPC) algorithm are presented in this work. The proposed approach to AFTC exploits the advantages of the MPC-RNN algorithm properties as well as accounting explicitly for severe control actuator faults in the nonlinear AUV model with uncertainties that are formulated by the MPC

Concepts of GPCR-controlled navigation in the immune system

G-protein-coupled receptor (GPCR) signaling is essential for the spatiotemporal control of leukocyte dynamics during immune responses. For efficient navigation through mammalian tissues, most leukocyte types express more than one GPCR on their surface and sense a wide range of chemokines and chemoattractants, leading to basic forms of leukocyte movement (chemokinesis, haptokinesis, chemotaxis, haptotaxis, and chemorepulsion). How leukocytes integrate multiple GPCR signals and make directional decisions in lymphoid and inflamed tissues is still subject of intense research. Many of our concepts on GPCR-controlled leukocyte navigation in the presence of multiple GPCR signals derive from in vitro chemotaxis studies and lower vertebrates. In this review, we refer to these concepts and critically contemplate their relevance for the directional movement of several leukocyte subsets (neutrophils, T cells, and dendritic cells) in the complexity of mouse tissues. We discuss how leukocyte navigation can be regulated at the level of only a single GPCR (surface expression, competitive antagonism, oligomerization, homologous desensitization, and receptor internalization) or multiple GPCRs (synergy, hierarchical and non-hierarchical competition, sequential signaling, heterologous desensitization, and agonist scavenging). In particular, we will highlight recent advances in understanding GPCR-controlled leukocyte navigation by intravital microscopy of immune cells in mice