Modeling and Control of a Surface Vessel for "ITS for the Sea" Applications A dissertation submitted to the Department of Electrical Engineering, University of Moratuwa in partial fulfilment of the requirements for the degree of Master of Science by KUDABADU JAYAWICKRAMA CHAMINDA KUMARA LIBRARY UNIVERSITY OF MORATUWA, SRI UNKA MORATUWA University of Moratuwa 89426 Department of Electrical Engineering University of Moratuwa, Sri Lanka September 2007 89426 Supervised by: Dr. Sisil Kumarawadu DECLARATION The work submitted in this dissertation is the result of my own investigation, except where otherwise stated. It has not already been accepted for any degree, and is also not being concurrently submitted for any other degree. K.J.C. Kumara 07th of September, 2007 I endorse the declaration by the candidate. Research Supervisor Dr. Sisil Kumarawadu Senior Lecturer in Electrical Engineering BSc Eng(Moratuwa), MEng(Saga), PhD(Saga) CONTENTS Declaration i Abstract iv Acknowledgement v List of Figures vi List of Tables vii 1. Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Goals of present work 3 2. Design Features of the Surface Vessel 4 2.1 Hulls 4 2.2 Strut 4 2.3 Submerged body (Gertler pontoon) 5 2.4 Propellers 5 3. Kinematics Analysis 6 3.1 Coordinate frames 6 3.2 Kinematics 7 3.3 Equation of motion 7 4. Detail Dynamics Analysis 9 4.1 Analysis of Hydrodynamics damping forces and moments 9 4.2 Damping forces on hulls . 11 4.3 Strut damping forces 12 4.4 Gertler pontoon damping forces 13 4.5 Thrust and propulsion 13 4.6 Propeller-hull interaction 15 4.7 Unsteady forces 15 4.8 Added mass coefficients 16 5. Application of a Computed Torque-like Controller 17 5.1. State Space Representation 17 5.2. CTC-like Controller 17 5.3. Desired trajectory 18 n 5.4. Simulation Results 19 6. Model Based Speed Control 20 6.1 Speed control problem 20 • 6.2 Dynamic equations 21 6.3 Stable speed control 23 6.4 Calculating real control inputs 25 6.5 Simulation Results 25 6.6 Conclusion 28 7. NN Adaptive Controller Design 29 7.1 Nonlinear control problem 29 7.2 Formulation of Dynamic equations 29 7.3 Control approach 30 7.4 Radial Basis Function neural networks 31 7.5 Updating of connecting weights 32 7.6 Measuring and estimating the Motion variables 34 7.7 Simulation Results 35 7.8 Conclusion 38 8. Shape Adaptive RBF NNs Controller Design 39 8.1 The NN model 39 8.2 Weights update for Guaranteed tracking performance 40 8.3 Longitudinal and Lateral neural subnets 43 8.4 Conclusion 43 9. Conclusions and Future Works • 44 9.1 Conclusions 44 9.2 Recommendations for Future Researches 45 References 46 Appendix A Assumptions 49 Appendix B Matlab programs 50 iii Abstract In the emerging field of intelligent transportation systems (ITS), "ITS for the sea" refers to the area of maritime traffic. Automated vehicle control systems are a key technology for ITS. An autonomous surface vessel (ASV) can be defined as a vehicle controlling its own steering and speed for Navigation, dynamic positioning, motion stabilization and obstacle detection and avoidance. The scope of the research is defined by two main objectives viz. developing complete mathematical model of a surface vessel by analyzing hydrodynamic forces and main other effects arising when manoeuvring in the ocean, and design online-learning adaptive controller for path tracking and speed control using real control inputs; propeller thrust and steering angle. The vessel moves in a hydrodynamic environment where many uncertainties, non-linear and non-predictive behaviours always appear. The ocean vehicle is modelled mathematically using first principles and derivations wherever possible. In this work, the problem of control with guaranteed sway and yaw stability for automated surface vessel operation is addressed with special emphasis on speed control. A control scheme to solve this problem without simplifying the dynamics is proposed and extensively studied using formative mathematical analyses and simulations. The main academic motivation of this research was to study and synthesis the power of artificial intelligence techniques in controlling of non-linear dynamical systems with online-learning and adaptive capabilities. A model-based neural network adaptive controller is developed blending a self adaptive neural network module and a classical Proportional plus Derivative (PD)-like control to obtain optimum control performance by complementing each other. The adaptive neural module counteracts for inherent model discrepancies, strong nonlinearities and coupling effects. | iwm ACKNOWLEDGEMENTS I would like to thank... DR. SISIL KUMARAWADU For the outstanding job he did as my Research Supervisor. For his academic brilliance, dedication, patience, and understanding. DR. D.H.S. MAITHREEPALA AND DR. NALIN WICKRAMARACHCH1 For the effort they put into reading and correcting my thesis as members of the final evaluation panel. PROFESSOR. RANJITH PERERA AND DR.LANKA UDAWATTA For the necessary guidance and invaluable advices during progress evaluations MR. AMARANATH PREMASIRI Granting me a valuable text book and sending many research papers ADIMINTRATION OF UNIVERSITY OF RUHUNA For funding me to read a M.Sc. degree MY FAMILY For their continual support, and for keeping me relatively sane and stable. MY WIFE For spoiling me with breakfast in the morning, free laundry services and an open ear even in the middle of the night. EVERYONE AT DEPARTMENT OF MECHANICAL & MANUFACTURING ENGINEERING, UNIVERSITY OF RUHUNA AND DEPARTMENT OF ELECTRICAL ENGINEERING, UNIVERSITY OF MORATUWA For supporting me in various ways List of Figures Figure Page 2.1 3D view of preliminary design of the ASV 4 3.1 Coordinate frames 6 4.1 Sketch of a propeller and its shaft 14 5.1 Tracking of a circular trajectory 19 5.2 Velocity diagrams 19 6.1 ASV with directions of interest for speed control 20 6.2 FBD of the surface vessel 21 6.3 Simulation results: Velocities 26 6.4 Simulation results: Net Thrust & Steering Angle 27 7.1 Architecture of the RBF neural network 31 7.2 Desired Octomorphic path 35 7.3 Tracking of a octomorphic trajectory 36 7.4 Position errors inXe- direction 36 7.5 Position errors in Ye- direction 37 7.6 Online adaptive process of Weights in X subnet 37 7.7 Online adaptive process of Weights in Y subnet 38 VI List of Tables Table Page 2.1 Body dimensions of the ASV 5 2.2 Fluid properties 5 4.1 Form factor 10 Vlll