Abstract
Extensive research has been conducted on a navigation system for inland vessels at the University of Stuttgart and at the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg (Focus Max Planck Gesellschaft, Computer at the helm, http://www.mpg.de/942027). As part of this navigation system, a model-based track-keeping controller has been developed. A high performance controller is required because of the reduced space available on rivers and canals. The control structure consists of two components, a feedback and a feedforward block, where the former is provided by a linear quadratic gaussian controller. Both components require the ship dynamics model and thus, the parameter estimation of the underlying model is a key issue to achieve high performance. In this chapter, we firstly consider Monte Carlo simulations to generate data of the closed-loop system and then the parameters of a continuous-time steering dynamics model are identified. The parameter estimation problem is solved applying an instrumental variable method, which takes into account the control structure. Parameter identification using real closed-loop experiments is also considered. Additionally, we evaluate the experiments for parameter estimation through a sensitivity analysis.
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References
Anderson BDO, Moore JB. Optimal control: Linear quadratic methods. Englewood Cliffs: Prentice Hall; 1989.
Åström KJ, Källström CG. Identification of ship steering dynamics. Automatica. 1976;12:9–22.
Bastogne T, Garnier H, Sibille P. A PMF-based subspace method for continuous-time model identification. Application to a multivariable winding process. Int J Control. 2001;74(2):118–32.
Bittner R. Modellbasierte Bahnführung von Binnenschiffen. Ph.D. dissertation, Otto von Guericke Universität Magdeburg (in preparation).
Bittner R, Gilles ED. Automatic track-keeping of inland vessels in flowing waterways. In: 4th international symposium on automatic control, Wismar, September 2005. Hochschule Wismar; 2005.
Bittner R, Driescher A, Gilles ED. Entwurf einer Vorsteuerung zur hochgenauen Bahnführung von Binnenschiffen. In: Wismarer Automatisierungssymposium, vol. 3, Wismar, 2002.
Bittner R, Driescher A, Gilles ED. Drift dynamics modeling for automatic track-keeping of inland vessels. In: Peshekhonov VG, editor. 10th Saint Petersburg international conference on integrated navigation systems, Saint Petersburg, May 2003. State Research Center of the Russian Federation “Elektropribor”; 2003. p. 218–27.
Blanke M, Knudsen M. Optimized experiment design for identification of marine systems. In: 14th IFAC world congress, Beijing, 1999.
Bolk S. Entwurf einer lqg-regelung zur bahnführung von binnenschiffen. Diplomarbeit, Universität Stuttgart, 2004.
Focus Max Planck Gesellschaft. Computer at the helm. http://www.mpg.de/942027.
Fossen TI. Guidance and control of ocean vehicles. New York: Wiley; 1994.
Fossen TI. Marine control systems. Marine Cybernetics; 2002.
Garnier H, Gilson M, Zheng WX. A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems. Int J Control. 2000;73(1):38–48.
Gilson M, Garnier H. Continuous-time model identification of systems operating in closed-loop. In: 13th IFAC symposium on system identification, 2003.
Gilson M, Van den Hof P. On the relation between a bias-eliminated least-squares (bels) and an iv estimator in closed-loop identification. Automatica. 2001;37:1593–600.
Gilson M, Van den Hof P. IV methods for closed-loop system identification. In: 13th IFAC symposium on system identification, 2003.
Gilson M, Van den Hof P. Instrumental variable methods for closed-loop system identification. Automatica. 2005;41:241–49.
Gilson M, Garnier H, Young P, Van den Hof P. Instrumental variable methods for closed-loop continuous-time model identification. In: Garnier H, Wang L, editors. Identification of continuous-time models from sampled data. New York: Springer; 2008. p. 133–60.
Gilson M, Garnier H, Young P, Van den Hof P. Optimal instrumental variable method for closed-loop identification. IET Control Theory Appl. 2011;5(10):1147–54.
Gronarz A. Rechnerische Simulation der Schiffsbewegung beim Manövrieren unter besonderer Berücksichtigung der Abhängigkeit von der Wassertiefe. Ph.D. thesis, Gerhard-Mercator-Universität Gesamthochschule Duisburg, 1997. http://duepublico.uni-duisburg-essen.de/servlets/DerivateServlet/Derivate-5111/inhalt.htm.
Herzer B. Die automatische bahnführung von schiffen mit nichtminimalphasiger dynamik. Ph.D. dissertation, Universität Stuttgart (in preparation).
Herzer B, Gilles ED. Disturbance estimation for feedforward control of inland vessels. In: 8th IFAC conference on control applications in marine systems, Rostock-Warnemünde, 2010.
Lachmeyer A, Herzer B, Gilles ED. Path planning for lock entering maneuvers using nonlinear programming. In: 8th IFAC conference on control applications in marine systems, Rostock-Warnemünde. IFAC; 2010.
Ljung L. System identification, theory for the user. 2nd ed. Englewood Cliffs: Prentice Hall; 1999.
Ljung L. Experiments with identification of continuous time models. In: 15th IFAC symposium of system identification, Saint-Malo, 2009.
Lutz A. Kollisionserkennung und -vermeidung auf Binnenwasserstraßen. Ph.D. thesis, Universität Stuttgart, 2011. http://elib.uni-stuttgart.de/opus/volltexte/2011/6399/.
Lutz A, Gilles ED. Opportunities for Automated Inland Navigation. In: Konings R, Priemus H, Nijkamp P, editors. The future of automated freight transport: Concepts, design and implementation. Edward Elgar Publishing; 2005. p. 51–64.
Nomoto K, Taguchi T, Honda K, Hirano S. On the steering qualities of ships. Int Shipbuild Prog. 1957;4:354–70.
Padilla A, Yuz JI, Herzer B. Continuous-time system identification of the steering dynamics of a ship on a river. Int J Control. 2014 (to appear).
Rawlings JB, Ekerdt JG. Chemical reactor analysis and design fundamentals. Nob Hill Publishing; 2002.
Tóth R, Laurain V, Gilson M, Garnier H. Instrumental variable scheme for closed-loop lpv model identification. Automatica. 2012;48:2314–20.
Wahl A. Einsatz optimaler Regelverfahren zur automatischen Bahnführung. Ph.D. thesis, Universität Stuttgart, 2001.
Young PC. Recursive estimation and time-series analysis: An introduction for the student and practitioner. Berlin: Springer; 2011.
Young PC, Garnier H, Gilson M. Refined instrumental variable identification of continuous-time hybrid Box-Jenkins models. In: Garnier H, Wang L, editors. Identification of continuous-time models from sampled data. Berlin: Springer; 2008. p. 91–131.
Zimmermann R. Repräsentation dynamischer Schiffsmodelle in einem Navigationssystem für die Binnenschiffahrt. Ph.D. thesis, Universität Stuttgart, 2000.
Acknowledgements
The authors thank Professor Ernst Dieter Gilles and the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg-Germany where Arturo Padilla did part of the work presented in this chapter and from where the navigation data was obtained. The presented research on modeling and control was conducted by Ralph Bittner under the direction of Professor Gilles at the Max Planck Institute.
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Padilla, A., Bittner, R., Yuz, J.I. (2015). Closed-Loop Identification and Control of Inland Vessels. In: Ocampo-Martinez, C., Negenborn, R. (eds) Transport of Water versus Transport over Water. Operations Research/Computer Science Interfaces Series, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-319-16133-4_18
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DOI: https://doi.org/10.1007/978-3-319-16133-4_18
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