Abstract
A robust adaptive autopilot for uninhabited surface vehicles (USV) based on a model predictive controller (MPC) is presented in this paper. The novel autopilot is capable of handling sudden changes in system dynamics. In real life situations, very often a sudden change in dynamics results in missions being aborted and the uninhabited vehicles have to be rescued before they cause damage to other marine craft in the vicinity. This problem has been suitably dealt with by this innovative design. The MPC adopts an online adaptive nature by utilising three algorithms, individually: gradient descent, least squares and weighted least squares (WLS). Even with random initialisation, significant improvements over the other algorithmic approach were achieved by WLS by maintaining the intermittent continuous values of system parameters and periodically reinitialising the covariance matrix. Also, a time frame of 25 seconds appears to be the optimum to reinitialise the parameters in simulation studies. This novel approach enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions.
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Eshel, T.: US Navy tests Rafael Spike missiles on unmanned vessels. http://defense-update.com 31 October (2012). Accessed 30 May 2013
Park, S., Kim, J., Lee, W., Jang, C.: 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 (2005)
Alves, J., Oliveira, P., Oliveira, R., Pascoal, A., Rufino, M., Sebastiao, L., Silvestre, C.: Vehicle and mission control of the Delfim autonomous surface craft. In: Proceedings of 14th Mediterranean Conference on Control Automation. Ancona (2006)
Elkaim, G.H., Kelbley, R.: 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 (2006)
Naeem, W., Sutton, R., Chudley, J.: Soft computing design of a linear quadratic Gaussian controller for an unmanned surface vehicle. In: Proceedings of 14th Mediterranean Conference on Control Automation. Ancona (2006)
Ashrafiuon, H., Muske, K.R., McNinch, L.C., Soltan, R.A.: Sliding-mode tracking control of surface vessels. IEEE Trans. Ind. Electron. 55(11), 4004–4012 (2008)
Qiaomei, S., Guang, R., Jin, Y., Xiaowei, Q.: Autopilot design for unmanned surface vehicle tracking control. In: Proceedings of 3rd International Conference on Measuring Technology and Mechatronics Automation. 1, January. Shangshai (2011)
Sharma, S.K., Naeem, W., Sutton, R.: An autopilot based on a local control network design for an unmanned surface vehicle. J. Navig. 65(2), 281–301 (2012)
Naeem, W., Xu, T., Sutton, R., Tiano, A.: The design of a navigation, guidance, and control system for an unmanned surface vehicle for environmental monitoring. Proc. Inst. Mech. Eng. M J. Eng. Marit. Environ 222(M2), 67–80 (2008)
Li, Z., Cao, X., Ding, N.: Adaptive fuzzy control for synchronization of nonlinear teleoperators with stochastic time-varying communication delays. IEEE Trans. Fuzzy Syst. 19(4), 745–757 (2011)
Liu, Y.J., Tong, S., Chen, C.L.P.: Adaptive fuzzy control via observer design for uncertain nonlinear systems with un-modelled dynamics. IEEE Trans. Fuzzy Syst 21(2), 275–288 (2013)
Li, Z., Ding, L., Gao, H., Duan, G., Su, C.Y.: Trilateral tele-operation of adaptive fuzzy force/motion control for nonlinear tele-operators with communication random delays. IEEE Trans. Fuzzy Syst. 21(4), 610–624 (2013)
Liu, Y.J., Chen, C.L.P., Wen, G.X., Tong, S.: Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems. IEEE Trans. Neural Netw. 22(7), 1162–1167 (2011)
Liu, Y.J., Tong, S.C, Wang, D., Li, T.S., Chen, C.L.P.: Adaptive neural output feedback controller design with reduced-order observer for a class of uncertain nonlinear SISO systems. IEEE Trans. Neural Netw. 22(8), 1328–1334 (2011)
Li, Z., Su, C.Y.: Neural-adaptive control of single-master multiple slaves tele-operation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainty. IEEE Trans. Neural Netw. Learn. Syst. 24(9), 1400–1413 (2013)
Chen, C.L.P., Liu, Y.J., Wen, G.X.: Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems. IEEE Trans. Cybern. 99, 2168–2267 (2013). doi:10.1109/TCYB.2013.2262935
Chen, M., Ge, S.S., Ren, B.: Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints. Automatica 47(3), 452–465 (2011)
Li, Z., Li, J., Kang, Y.: Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments. Automatica 46(12), 2028–2034 (2010)
Cui, R., Ge, S.S., How, V.E.B., Choo, Y.S.: Leader–follower formation control of underactuated autonomous underwater vehicles. Ocean Eng. 37(17–18), 1491–1502 (2010)
Cui, R., Ren, B., Ge, S.S.: Synchronised tracking control of multi-agent system with high order dynamics. IET Control Theory Appl. 6(5), 603–614 (2012)
Annamalai, A.S.K.: A review of model predictive control and closed loop system identification for design of an autopilot for uninhabited surface vehicles (2012) Springer Technical Report: MIDAS.SMSE.2012.TR.005 (2012)
Maciejowski, JM.: Predictive control with constraints. Prentice Hall Inc., London (2002)
Allgower, F., Glielmo, L., Guardiola, C., Kolmanovsky, I.: Automotive model predictive control. Springer-Verlag, Berlin (2010)
Rawlings, JB, Mayne, D Q: Model predictive control: Theory and design. Nob Hill Publishing, Madison (2009)
Wang, L.: Model predictive control system design and implementation using MATLAB. Springer-Verlag, Berlin (2009)
Li, Z., Sun, J.: Disturbance compensating model predictive control with application to ship heading control. IEEE Trans. Control Syst. Technol. 20(1), 257–265 (2012)
Liu, J., Allen, R., Yi, H.: Ship motion stabilizing control using a combination of model predictive control and an adaptive input disturbance predictor. Proc. IMechE Part I J. Syst. Control Eng. 225(5), 591–602 (2011)
Oh, S.R., Sun, J.: Path following of under actuated marine surface vessels using line-of-sight based model predictive control. Ocean Eng. 37(2–3), 289–295 (2010)
Naeem, W., Sutton, R., Chudley, J., Dalgleish, F.R., Tetlow, S.: An online genetic algorithm based model predictive control autopilot design with experimental verification. Int. J. Control 78(14/20), 1076–1090 (2005)
Perez, T.: Ship motion: Course keeping and roll stabilisation using rudder and fins. Springer-Verlag, London (2005)
Ljung, L.: System identification, theory for the user, 2nd edn. Prentice Hall, New Jersey (1999)
Annamalai, A.S.K., Motwani, A.: A comparison between LQG and MPC autopilots for inclusion in a navigation, guidance and control system. Springer Technical Report, MIDAS SMSE.2013.TR. 006. Plymouth University, Plymouth (2013)
Annamalai, A.S.K., Motwani, A., Sutton, R., Yang, C., Sharma, S K, Culverhouse, P.: Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control. In: Proceedings of the 1st IET Control and Automation Conference, Conference Aston Lakeside Centre. Birmingham (2013)
Guo, L.: Self-convergence of weighted least squares with applications to stochastic adaptive control. IEEE Trans. Autom. Control 41(1), 79–89 (1996)
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Annamalai, A.S.K., Sutton, R., Yang, C. et al. Robust Adaptive Control of an Uninhabited Surface Vehicle. J Intell Robot Syst 78, 319–338 (2015). https://doi.org/10.1007/s10846-014-0057-2
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DOI: https://doi.org/10.1007/s10846-014-0057-2
Keywords
- Uninhabited surface vehicles
- Gradient descent
- Least squares
- Weighted least squares
- Adaptive control
- Robust control