Path following control of underactuated autonomous vessels remains a challenging issue in recent years due to its inherent underactuation and nonlinearities as well as the widely existing disturbances in the marine environment. In order to accommodate all the difficulties simultaneously, a novel extended state Kalman filter, which adopts the idea of extended state observer in estimating and compensating system lumped disturbance and optimizes the filter gain in a real-time fashion using Kalman filter technique, is constructed to estimate system states and disturbances in the presence of model uncertainties and measurement noise. Based on the estimated states and disturbances, an enhanced model predictive controller is proposed to steer the underactuated autonomous vessels along a predefined path at a desired speed after considering system state and input constraints. Simulation results have proved the superiority of extended state Kalman filter over traditional extended state observer and extended Kalman filter under various disturbance and noise scenarios. Moreover, the comparison results with conventional proportion-integration-differentiation controller have demonstrated the feasibility and efficacy of the proposed extended state Kalman filter-based model predictive controller in both set-point tracking and disturbance rejection.
BaiWXueWHuangY, et al. (2018) On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems. SCIENCE CHINA Information Sciences61(4): 1–16.
2.
DoKDPanJ (2006) Global robust adaptive path following of underactuated ships. Automatica42(10): 1713–1722.
3.
DoKDPanJJiangZP (2004) Robust and adaptive path following for underactuated autonomous underwater vehicles. Ocean Engineering31(16): 1967–1997.
4.
HanJ (2009) From PID to Active Disturbance Rejection Control. IEEE Transactions on Industrial Electronics56(3): 900–906.
5.
HuangYZhangYWuZ, et al. (2017a) A novel robust student’s T-based Kalman filter. IEEE Transactions on Aerospace and Electronic Systems53(3): 1545–1554.
6.
HuangYZhangYWuZ, et al. (2018a) A novel adaptive kalman filter with inaccurate process and measurement noise covariance matrices. IEEE Transactions on Automatic Control63(2): 594–601.
7.
HuangYZhangYXuB, et al. (2017b) A new outlier-robust student’s t-based Gaussian approximate filter for cooperative localization. IEEE/ASME Transactions on Mechatronics22(5): 2380–2386.
8.
HuangYZhangYXuB, et al. (2018b) A new adaptive extended Kalman filter for cooperative localization. IEEE Transactions on Aerospace and Electronic Systems54(1): 353–368.
9.
HuangYZhangYZhaoY, et al. (2019) A novel robust Gaussian-student’s mixture distribution based Kalman filter. IEEE Transactions on Signal Processing67(13): 3606–3620.
10.
LapierreLSoetantoD (2007) Nonlinear path-following control of an AUV. Ocean Engineering34(11–12): 1734–1744.
11.
LiZSunJ (2012) Disturbance compensating model predictive control with application to ship heading control. IEEE Transactions on Control Systems Technology20(1): 257–265.
12.
LiZSunJOhS (2009a) Design, analysis and experimental validation of a robust nonlinear path following controller for marine surface vessels. Automatica45(7): 1649–1658.
13.
LiZSunJOhS (2009b) Path following for marine surface vessels with rudder and roll constraints: An MPC approach. In: 2009 American Control Conference, St Louis, MO, 10–12, June 2009, pp. 3611–3616. Piscataway, NJ: IEEE.
14.
ListakMMartinGPugalD, et al. (2005) Design of a semiautonomous biomimetic underwater vehicle for environmental monitoring. In: 2005 International Symposium on Computational Intelligence in Robotics and Automation, Espoo, Finland, 27–30, June 2005, pp. 9–14. Piscataway, NJ: IEEE.
15.
LiuCNegenbornRRChuX, et al. (2018) Predictive path following based on adaptive line-of-sight for underactuated autonomous surface vessels. Journal of Marine Science and Technology23(3): 483–494.
16.
LiuCNegenbornRRZhengH, et al. (2017) A state-compensation extended state observer for model predictive control. European Journal of Control36: 1–9.
17.
MadońskiRHermanP (2015) Survey on methods of increasing the efficiency of extended state disturbance observers. ISA Transactions56: 18–27.
18.
MayneDQRawlingsJBRaoCV, et al. (2000) Constrained model predictive control: Stability and optimality. Automatica36(6): 789–814.
19.
OhS-RSunJ (2010) Path following of underactuated marine surface vessels using line-of-sight based model predictive control. Ocean Engineering37(2–3): 289–295.
20.
PavlovANordahlHBreivikM (2009) MPC-based optimal path following for underactuated vessels. IFAC Proceedings Volumes42(18): 340–345.
21.
PengYHanJSongQ (2007) Tracking control of underactuated surface ships: Using unscented Kalman filter to estimate the uncertain parameters. In: 2007 International Conference on Mechatronics and Automation, Harbin, China, 5–8, August 2007, pp. 1884–1889. Piscataway, NJ: IEEE.
22.
PengZWangJ (2018) Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems48(4): 535–544.
23.
PengZWangJWangJ (2019) Constrained control of autonomous underwater vehicles based on command optimization and disturbance estimation. IEEE Transactions on Industrial Electronics66(5): 3627–3635.
24.
PeymaniEFossenTI (2015) Path following of underwater robots using Lagrange multipliers. Robotics and Autonomous Systems67(C): 44–52.
25.
QingZGaolLQZhiqiangG (2007) On stability analysis of active disturbance rejection control for nonlinear time-varying plants with unknown dynamics. In: 2007 46th IEEE Conference on Decision and Control, LA, USA, 12–14, December 2007, pp. 3501–3506. Piscataway, NJ: IEEE.
26.
RibasDPalomerasNRidaoP, et al. (2012) Girona 500 AUV: From survey to intervention. IEEE/ASME Transactions on Mechatronics17(1): 46–53.
27.
ShenCShiYBuckhamB (2019) Path-following control of an AUV: A multiobjective model predictive control approach. IEEE Transactions on Control Systems Technology: 27(3): 1334–1342.
28.
VelagicJVukicZOmerdicE (2003) Adaptive fuzzy ship autopilot for track-keeping. Control Engineering Practice11(4): 433–443.
29.
WangX-FZouZ-JWangY, et al. (2010) Path following control of underactuated ships based on unscented Kalman filter. Journal of Shanghai Jiaotong University (Science)15(1): 108–113.
30.
WangYYanWGaoB, et al. (2009) Backstepping-based path following control of an underactuated autonomous underwater vehicle. In: 2009 International Conference on Information and Automation, Zhuhai/Macau, China, 22–24, June 2009, pp. 466–471. Piscataway, NJ: IEEE.
31.
XueWCZhangXCSunL, et al. (2020) Extended state filter based disturbance and uncertainty mitigation for nonlinear uncertain systems with application to fuel cell temperature control. IEEE Transactions on Industrial Electronics67(12): 10682–10692.
32.
YangCMarkMLarryM (2005) Visual odometry on the Mars exploration rovers. In: 2005 IEEE International Conference on Systems, Man and Cybernetics HI, USA, 12 October 2005, pp. 903–910. Piscataway, NJ: IEEE.
