The control of any underwater vehicle has always been a challenging task and is certainly an important and necessary feature of an autonomous underwater vehicle (AUV). This paper describes the implementation of a genetic algorithm (GA)-based model predictive controller for an AUV named Hammerhead which is being developed jointly by the Universities of Plymouth and Cranfield. To the present authors' knowledge, this is the first successful application of a GA in realtime optimization for controller tuning in the marine sector and thus the paper makes an extremely novel and useful contribution to control system design. The advantages of using model predictive control (MPC) include its constraint handling and disturbance rejection properties commonly present in an underwater environment. The use of GAs generalizes MPC to employ linear as well as nonlinear process models. Furthermore, it supports the inclusion of various types of objective function without having to change the controller structure. The model required for MPC is extracted using system identification techniques on actual AUV data obtained from full-scale in-water tests. A description of Hammerhead AUV is outlined and simulation and experiment data are shown which were obtained by optimizing the cost function online using the GA. Results demonstrate good tracking behaviour despite the presence of disturbances and ever-present modelling uncertainty.
McPhailS DPebodyMNavigation and control of an autonomous underwater vehicle using a distributed, networked, control architectureJ. Soc. Underwater Technol., 1998, 23 (1), 19–30.
4.
MillardN WGriffithsGFineganGMcPhailS DMeldrumD TPebodyMPerrettJ RStevensonPWebbA TVersatile autonomous submersibles—the realising and testing of a practical vehicleJ. Soc. Underwater Technol., 1998, 23 (1), 7–17.
5.
AsakawaKKojimaJItoYTakagiSAutonomous underwater vehicle AQUA EXPLORER 1000 for inspection of underwater cables. In Proceedings of the 1996 Symposium onAutonomous Underwater Vehicle Technology, Monterey, California, USA, 1996, pp. 10–17 (IEEE, New York).
6.
BalasuriyaAUraTAutonomous target tracking by underwater robots based on vision. In Proceedings of the IEEE Conference onUnderwater Technology (UT'98), Tokyo, Japan, April 1998, pp. 191–197 (IEEE, New York).
7.
ItoYKatoNKojimaJTakagiSCable tracking for autonomous underwater vehicle. In Proceedings of the 1994 Symposium onAutonomous Underwater Vehicle Technology, Cambridge, Massachusetts, USA, 1994, pp. 218–224 (IEEE, New York).
8.
KatoNItoYKojimaJTakagiSAsakawaKShirasakiYControl performance of autonomous underwater vehicle ‘AQUA EXPLORER 1000’ for inspection of underwater cables. In Proceedings of the Conference onOceans Engineering for Today's Technology and Tomorrow's Preservation, Brest, France, 1994, Vol. 1, pp. 1/135–I/140 (IEEE, New York).
9.
IshiiKFujiiTUraTAn on-line adaptation method in a neural network based control system for auvsIEEE J. Oceanic Engng, 1995, 20 (3), 221–228.
10.
OliveiraPSilvestreCAguiarAPascoalAGuidance and control of the SIRENE underwater vehicle: From system design to tests at sea. In Proceedings of the Conference onOceans, Nice, France, September 1998, pp. 1043–1048.
11.
AguiarAPascoalAModelling and control of an autonomous underwater shuttle for the transport of benthic laboratories. In Proceedings of the Conference onOceans, Halifax, Canada, October 1997, pp. 888–895 (MTS-IEEE, New York).
12.
HealeyA JLienardDMultivariable sliding model control for autonomous diving and steering of unmanned underwater vehiclesIEEE J. Oceanic Engng, July 1993, 18 (3), 327–339.
13.
HealeyA JMarcoD BCommand, Control and Navigation: Experimental Results with the NPS ARIES AUVIEEE J. Oceanic Engineering, October 2001, 26 (4), 466–477.
14.
TinkerS JBowmanA RBoothT BIdentifying submersible dynamics from free model experimentsRINA An. Rep. Trans., 191–196, 1979.
15.
IppolitC GRadicioniSRossoliniAMultiple models control of a remotely operated vehicle: Analysis of models structure and complexity. In Proceedings of the IFAC Conference onControl Applications in Marine Systems (CAMS'2001), Glasgow, Scotland, 2001 (IFAC).
16.
GoheenK RJefferysE RThe application of alternative modelling techniques to ROV dynamics. In Proceedings of the IEEE International Conference onRobotics and Automation, Cincinnati, Ohio, USA, May 1990, pp. 1302–1309 (IEEE, New York).
17.
AhmadS MSuttonRDynamic modelling of a remotely operated vehicle. In Proceedings of the 1st IFAC Workshop on theGuidance and Control of Underwater Vehicles (GCUV 2003), Newport, South Wales, April 2003, pp. 47–52 (IFAC).
18.
BossleyK MBrownMHarrisC JNeurofuzzy identification of an autonomous underwater vehicleInt. J. System Sci., 1999, 30 (9), 901–913.
19.
KimJKimKChoiH SSeongWLeeK YEstimation of hydrodynamic coefficients for an AUV using nonlinear observersIEEE J. Oceanic Engng, October 2002, 27 (4), 335–348.
20.
KwiesielewiczMPiotrowskiWSuttonRPredictive versus fuzzy control of autonomous underwater vehicle. In Proceedings of the IEEE International Conference onMethods and Models in Automation and Robotics, Miedzyzdroje, Poland, August 2001, pp. 609–612 (IEEE, New York).
21.
NaeemWModel predictive control of an autonomous underwater vehicle. In Proceedings of the UKACC Conference onControl, September 2002, pp. 19–23 (UKACC, Sheffield).
22.
NaeemWSuttonRAhmadS MPure pursuit guidance and model predictive control of an autonomous underwater vehicle for cable/pipeline tracking (invited paper). Presented at the World Maritime Technology Conference, San Francisco, California, USA, 17–20 October 2003, Paper B1.
23.
BarridoS CDadiosE POnline robot tracking using genetic algorithms. In Proceedings of the IEEE International Symposium onIntelligent Control, Vancouver, Canada, October 2002, pp. 479–484 (IEEE, New York).
24.
ChouW DLinF JShyuK KIncremental motion control of an induction motor servo drive via a genetic-algorithm-based sliding mode controllerProc. Instn Electl Engrs Part D: Control Theory Appl., May 2003, 150 (3), 209–220.
25.
DalgleishF RTetlowSAllwoodR LThe design of a laser-based imaging sensor for AUV navigation. In Institute of Physics PHOTON 02 Exhibition, Cardiff, Wales, September 2002.
26.
DalgleishF RTetlowSAllwoodR LPreliminary experiments in the development of a laser-based imaging sensor for AUV navigation. In Proceedings of the Conference onGuidance and Control of Underwater Vehicles (GCUV 2003), Newport, Wales, April 2003, pp. 239–244.
27.
LoebisDDalgleishF RSuttonRTetlowSChudleyJAllwoodR LAn integrated approach in the design of a navigation system for an AUV. In Proceedings of the Conference (MCMC 2003), Girona, Spain, September 2003, pp. 319–324.
28.
LjungLSystem Identification, Theory for the User, 2nd edition, 1999 (Prentice-Hall, Englewood Cliffs, New Jersey).
29.
QinS JBadgewellT AAn overview of nonlinear model predictive control applications In Nonlinear Model Predictive Control (Edited by AllgöwerFZhengA), 1999 (Birkhäuser, Switzerland), pp. 369–392.
CutlerC RRamakerB LDynamic matrix control—a computer control algorithm. In Proceedings of the Joint Automatic Control Conference, San Francisco, California, USA, August 1980, Paper WP5-B.
32.
ClarkeD WMohtadiCTuffP SGeneralised predictive control. Part 1: The basic algorithmAutomatica, 1987, 23 (2), 137–148.
33.
ClarkeD WMohtadiCTuffP SGeneralised predictive control. Part 2: Extensions and interpretationsAutomatica, 1987, 23 (2), 149–160.
SoeterboekRPredictive Control, A Unified Approach, 1992 (Prentice-Hall, Englewood Cliffs, New Jersey).
39.
RichaletJIndustrial applications of model based predictive controlAutomatica, 1993, 29 (5), 1251–1274.
40.
RawlingsJ BTutorial overview of model predictive controlIEEE Control Systems Mag., June 2000, 20 (3), 38–52.
41.
SaitS MYoussefHIterative Computer Algorithms with Applications in Engineering, Solving Combinatorial Optimization Problems, 1999 (IEEE Computer Society, Los Alamitos, CA, USA).
42.
DuwaishHNaeemWNonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms. In Proceedings of the 2001 IEEE International Conference onControl Applications (CCA01), Mexico City, Mexico, September 2001, pp. 465–469 (IEEE, New York).
43.
LoebisDSuttonRChudleyJNaeemWAdaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation systemControl Engng Practice, 2004 (accepted for publication).
