Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design.
YanRJPangSSunHB. Development and missions of unmanned surface vehicle. J Mar Sci Appl2010; 9: 451–457.
3.
MajohrJBuchTKorteC. Navigation and automatic control of the measuring dolphin (MESSINTM). In: 5th IFAC conference on manoeuvring and control of marine craft, Aalborg, Denmark, 23–25 August 2000, pp.405–410.
4.
CacciaMBibuliMBonoR. Unmanned surface vehicle for coastal and protected waters applications: the Charlie project. Mar Technol Soc J2007; 41: 62–71.
5.
ParkSKimJLeeW. A study on the fuzzy controller for an unmanned surface vessel designed for sea probes. In: Proceedings of international conference on control, automation and systems, Kintex, Korea, 2–5 June 2005, pp.1–4.
6.
AlvesJOliveiraPOliveiraR. Vehicle and mission control of the DELFIM autonomous surface craft. In: Proceedings of 14th Mediterranean conference on control automation, Ancona, Italy, 28–30 June 2006, pp.1–6. IEEE.
7.
ElkaimGHKelbleyR. Measurement based H infinity controller synthesis for an autonomous surface vehicle. In: Proceedings of 19th international technical meeting of the satellite division of the institute of navigation, Fort Worth, TX, 26–29September2006, pp.1973–1982.
8.
NaeemWSuttonRChudleyJ. Soft computing design of a linear quadratic Gaussian controller for an unmanned surface vehicle. In: Proceedings of 14th Mediterranean conference on control automation, Ancona, Italy, 28–30 June 2006, pp.1–6. IEEE.
9.
AshrafiuonHMuskeKRMcNinchLC. Sliding-mode tracking control of surface vessels. IEEE T Ind Electron2008; 55: 4004–4012.
10.
QiaomeiSGuangRJinY. Autopilot design for unmanned surface vehicle tracking control. In: Proceedings of 3rd international conference on measuring technology and mechatronics automation, Shanghai, China, 6–7 January 2011, pp.610–613. IEEE.
11.
SharmaSSuttonR. An optimised nonlinear model predictive control based autopilot for an uninhabited surface vehicle. In: The 2013 IFAC intelligent autonomous vehicles symposium, Gold Coast, QLD, Australia, 26–28 June 2013, pp.73–78.
12.
NaeemWXuTSuttonR. The design of a navigation, guidance, and control system for an unmanned surface vehicle for environmental monitoring. Proc IMechE, Part M: J Engineering for the Maritime Environment2008; 222: 67–80.
13.
SivanandamSNDeepaSN. Introduction to genetic algorithms. Berlin: Springer-Verlag, 2008.
14.
SharmaSSuttonR. Modelling the yaw dynamics of an uninhabited surface vehicle for navigation and control systems design. Proc IMarEST, Part A: J Mar Eng Technol2012; 11: 9–20.
15.
TownsendSLightbodyGBrownMD. Nonlinear dynamic matrix control using local model networks. T I Meas Control1998; 20: 47–56.
16.
RippinDWT. Control of batch processes. In: Proceedings of the 3rd IFAC DYCORD +‘89 symposium, Maastricht, The Netherlands, 1989, pp.115–125.
17.
JohansenTAFossBA. Semi-empirical modeling of nonlinear dynamic systems through identification of operating regimes and local models. In HuntK.J..(eds.), Neural Network Engineering in Dynamic Control Systems, Springer-verlag London limited, 1995; pp 105–126.
18.
BrownMLightbodyGIrwinGW. Non-linear internal model control using local model networks. IEE P: Contr Theor Ap1997; 144: 505–514.
19.
SharmaSKMcLooneSIrwinGW. Genetic algorithms for local controller network construction. IEE P: Contr Theor Ap2005; 152: 587–597.
20.
MaciejowskiJ. Predictive control with constraints. London: Prentice Hall, Inc., 2002.
21.
RawlingsJBMayneDQ. Model predictive control: theory and design. Madison, WI: Nob Hill Publishing, 2009.
22.
WangL. Model predictive control system design and implementation using MATLAB. Berlin: Springer-Verlag, 2009.
23.
AllgowerFGlielmoLGuardiolaC. Automotive model predictive control. Berlin: Springer-Verlag, 2010.
24.
PerezT. Ship motion: course keeping and roll stabilisation using rudder and fins. London: Springer-Verlag, 2005.
25.
OhS-RSunJ. Path following of underactuated marine surface vessels using line-of-sight based model predictive control. Ocean Eng2010; 37: 289–295.
26.
LiuJAllenRYiH. Ship motion stabilizing control using a combination of model predictive control and an adaptive input disturbance predictor. Proc IMechE, Part I: J Systems and Control Engineering2011; 225: 591–602.
27.
LiZSunJ. Disturbance compensating model predictive control with application to ship heading control. IEEE T Contr Syst T2012; 20: 257–265.
28.
NaeemWSuttonRXuT. An integrated multi-sensor data fusion algorithm and autopilot implementation in an uninhabited surface craft. Ocean Eng2012; 39: 43–52.
29.
TatjewskiPLawrynczukM. Soft computing in model – based predictive control. Int J Appl Math Comp2006; 16: 7–26.
30.
GruneLPannekJ. Nonlinear model predictive control: theory and applications. London: Springer-Verlag, 2011.
31.
MartinseFBieglerLTFossBA. A new optimization algorithm with application to nonlinear MPC. J Process Contr2004; 14: 853–865.
32.
MoritzDFindeisenRAllgowerF. Stability of nonlinear model predictive control in the presence of errors due to numerical online optimization. In: Proceedings of the 42nd IEEE conference decision and control, Hawaii, 9–12 December 2003, pp.1419–1424. IEEE.
33.
FindeisenRDiehlMDisli-UsluI. Computation and performance assessment of nonlinear model predictive control. In: Proceedings of the 41st IEEE conference on decision and control, Las Vegas, NV, 10–13 December 2002, pp.4613–4618. IEEE.
34.
DiehlMFindeisenRSchwarzkopfS. An efficient algorithm for nonlinear predictive control of large-scale systems. Automatic Technol2002; 50: 557–567.
35.
TennyMJWrightSJRawlingsJB. Nonlinear model predictive control via feasibility-perturbed sequential quadratic programming. Comput Optim Appl2004; 28: 87–121.
36.
MichalskaHMayneDQ. Robust receding horizon control of constrained nonlinear systems. IEEE T Automat Contr1993; 38: 1623–1633.
37.
ChenWWuG. Nonlinear modeling of two-tank system and its nonlinear model predictive control. In: Proceedings of the 24th Chinese control conference, Guangzhou, China, 2005, pp.396–401.
38.
ChenWLiXChenM. Suboptimal nonlinear model predictive control based on genetic algorithm. In: Proceedings of the third international symposium on intelligent information technology application workshops, Nanchang, China, 21–22 November 2009, pp.119–124. IEEE.
39.
SharmaSKMcLooneSIrwinGW. Genetic algorithms for local model and local controller network design. In: Proceedings of IEEE American control conference, Alaska, 2002, paper ID ACC02-IEEE1255, pp.1693–1698. IEEE.
40.
OgataK. Modern control engineering. 4th ed.Prentice-Hall, Inc., 2002.
