A model-driven implementation to realize controllers for Autonomous Underwater Vehicles

Abstract The goal of this study is investigated an implementation model, which is based on the real-time Unified Modeling Language (UML)/MARTE combined with the specification of hybrid automata and the Extended Kalman Filter (EKF) algorithm in order to intensively capture the development lifecycle of control and reuse it for controllers of Autonomous Underwater Vehicles (AUVs). To achieve this goal, the study is stepwise carried out as follows: the physical and dynamic model together with control architecture of AUVs are firstly adapted for developing entirely an AUV controller. The use-case model combined with the realization hypotheses of hybrid automata and the EKF algorithm are then specialized to closely gather the requirement analysis of control. The specializations of real-time UML/MARTE’s features such as the ‘capsules, ports and protocols’ notation combined with the timing concurrency of evolution are next realized to precisely design structures and behaviors for the controller. The detailed design model is then converted into the implementation model by using object-oriented and open-source platforms in order to quickly simulate and realize this controller. Finally, a planar trajectory-tracking controller, which permits a miniature unmanned submarine to autonomously reaches and follows a planar reference trajectory, was deployed and tested with good reliability.

[1]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[2]  Mohammad Pourmahmood Aghababa,et al.  3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles , 2012 .

[3]  Mohd Rizal Arshad,et al.  A hybrid-driven underwater glider model, hydrodynamics estimation, and an analysis of the motion control , 2014 .

[4]  Hamid Reza Karimi A computational method for optimal control problem of time-varying state-delayed systems by Haar wavelets , 2006, Int. J. Comput. Math..

[5]  Benedetto Allotta,et al.  A new AUV navigation system exploiting unscented Kalman filter , 2016 .

[6]  Xingyu Wang,et al.  Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems , 2015 .

[7]  Elisabet Estevez,et al.  On the use of model-based techniques for achieving multi-mode control architectures , 2014 .

[8]  N. V. Hien,et al.  An object-unified approach to develop controllers for autonomous underwater vehicles , 2016 .

[9]  Alireza Khosravi,et al.  Model reference adaptive autopilot with anti-windup compensator for an autonomous underwater vehicle: Design and hardware in the loop implementation results , 2017 .

[10]  Gianluca Antonelli,et al.  Underwater robots: Motion and force control of vehicle , 2006 .

[11]  Alberto L. Sangiovanni-Vincentelli,et al.  Languages and Tools for Hybrid Systems Design , 2006, Found. Trends Electron. Des. Autom..

[12]  Muhammad Rashid,et al.  Toward the tools selection in model based system engineering for embedded systems - A systematic literature review , 2015, J. Syst. Softw..

[13]  Yuxin Zhao,et al.  Convergence Analysis on Multi-AUV Systems With Leader-Follower Architecture , 2017, IEEE Access.

[14]  João Quintas,et al.  AUV Terrain-Aided Navigation using a Doppler Velocity Logger★★ , 2015 .

[15]  Wanli Li,et al.  A novel backtracking navigation scheme for Autonomous Underwater Vehicles , 2014 .

[16]  B. Bett,et al.  Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience , 2014 .

[17]  Hamid Reza Karimi,et al.  Robust synchronization and fault detection of uncertain master-slave systems with mixed time-varying delays and nonlinear perturbations , 2011 .

[18]  Bruce Powel Douglass,et al.  Design Patterns for Embedded Systems in C: An Embedded Software Engineering Toolkit , 2010 .

[19]  Bruce Powel Douglass Real-Time UML Workshop for Embedded Systems , 2014 .

[20]  Thor I. Fossen,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control , 2011 .

[21]  Pouria Sarhadi,et al.  Extended and Unscented Kalman filters for parameter estimation of an autonomous underwater vehicle , 2014 .

[22]  Pere Ridao,et al.  I-AUV Mechatronics Integration for the TRIDENT FP7 Project , 2015, IEEE/ASME Transactions on Mechatronics.

[23]  Hamid Reza Karimi,et al.  A sliding mode approach to H∞ synchronization of master-slave time-delay systems with Markovian jumping parameters and nonlinear uncertainties , 2012, J. Frankl. Inst..

[24]  Bla Lantos,et al.  Nonlinear Control of Vehicles and Robots , 2010 .

[25]  Gianluca Palermo,et al.  The COMPLEX methodology for UML/MARTE Modeling and design space exploration of embedded systems , 2014, J. Syst. Archit..

[26]  Tapabrata Ray,et al.  A brief taxonomy of autonomous underwater vehicle design literature , 2014 .

[27]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[28]  Pravin Varaiya,et al.  What's decidable about hybrid automata? , 1995, STOC '95.

[29]  Paulo Cézar Stadzisz,et al.  A Brazilian survey on UML and model-driven practices for embedded software development , 2013, J. Syst. Softw..

[30]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[31]  J. Lottin,et al.  A remark on "Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity"' , 2002 .

[32]  Thor I. Fossen,et al.  Guidance Laws for Autonomous Underwater Vehicles , 2009 .

[33]  Thor I. Fossen,et al.  Integral LOS Path Following for Curved Paths Based on a Monotone Cubic Hermite Spline Parametrization , 2014, IEEE Transactions on Control Systems Technology.

[34]  Benedetto Allotta,et al.  A low cost autonomous underwater vehicle for patrolling and monitoring , 2017 .