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Trajectory Tracking and Re-planning with Model Predictive Control of Autonomous Underwater Vehicles

Published online by Cambridge University Press:  21 September 2018

Zhen Hu ,
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window] ,
Caicha Cui  and
Bing Sun
Zhen Hu
Affiliation:
(China Ship Scientific Research Center, Wuxi, China)
Daqi Zhu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Caicha Cui
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Bing Sun
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
*
(E-mail: [email protected])
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Abstract

The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.

Keywords

Autonomous Underwater VehiclesTrajectory Re-planningTrajectory TrackingObstacle AvoidanceModel Predictive Control
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 2 , March 2019 , pp. 321 - 341
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Abbas, M. A., Milman, R. and Eklund, J. M. (2017). Obstacle avoidance in real time with nonlinear model predictive control of autonomous vehicles. Canadian Journal of Electrical and Computer Engineering, 40, 12–22.Google Scholar
Bagheri, A. and Moghaddam, J. J. (2009). Simulation and tracking control based on neural-network strategy and sliding-mode control for under-water remotely operated vehicle. Neurocomputing, 72, 1934–1950.Google Scholar
Bahadorian, M., Savkovic, B., Eaton, R. and Hesketh, T. (2012). Robust model predictive control for automated trajectory tracking of an unmanned ground vehicle. American Control Conference, Montreal, Canada, 4251–4256.Google Scholar
Barth, A., Reichhartinger, M., Wulff, K., Horn, M. and Reger, J. (2016). Certainty equivalence adaptation combined with super-twisting sliding-mode control. International Journal of Control, 89(9), 1767–1776.Google Scholar
Bessa, W.M., Dutra, M.S. and Kreuzer, E. (2008). Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller. Robotics and Autonomous Systems, 56, 670–677.Google Scholar
Cui, M., Sun, D., Liu, W., Zhao, M. and Liao, X. (2012). Adaptive tracking and obstacle avoidance control for mobile robots with unknown sliding. International Journal of Advanced Robotic Systems, 9, 1–14.Google Scholar
Falcone, P. (2007). Nonlinear model predictive control for autonomous vehicles. Benevento: University of Sannio.Google Scholar
Fossen, T. I. (2002). Marine control system: guidance, navigation, and control of ships, rigs and underwater vehicles. Trondheim: Marine Cybernetics.Google Scholar
Hoseini, S. M., Farrokhi, M. and Koshkouei, A. J. (2008). Robust adaptive control of uncertain non-linear systems using neural networks. International Journal of Control, 81, 1319–1330.Google Scholar
Jia, H., Zhang, L., Cheng, X., Bian, X., Yan, Z. and Zhou, J. (2012). Three dimensional path following control for underatuated UUV based on nonlinear iterative sliding mode. Acta Automatica Sinica, 38, 308–314.Google Scholar
Kim, H. J., Shim, D. H. and Sastry, S. (2002). Nonlinear model predictive tracking control for rotorcraft-based unmanned serial vehicles. Proceedings of American Control Conference, Anchorage, USA, 3576–3581.Google Scholar
Li, Z., Deng, J., Lu, R., Xu, Y., Bai, J. and Su, C. (2016). Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach. IEEE Transactions on Systems, Man, and Cybernetics: System, 46, 740–949.Google Scholar
Luo, C. and Yang, S. X. (2008) A bio-inspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environment. IEEE Transactions on Neural Networks, 19, 1279–1298.Google Scholar
Miao, B., Li, T. and Luo, W. (2013). A DSC and MLP based robust adaptive NN tracking control for underwater vehicle. Neurocomputing, 111, 184–189.Google Scholar
Pan, C. Z., Lai, X. Z., Yang, S. X. and Wu, M. (2013). An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics. Expert Systems with Applications, 40, 1629–1635.10.1016/j.eswa.2012.09.008Google Scholar
Serdar, S., Bradley, J. B. and Ron, P. P. (2008). A chattering-free sliding-mode controller for underwater vehicles with fault-tolerant infinity-norm thrust allocation. Ocean Engineering, 35, 1647–1659.Google Scholar
Shen, C., Shi, Y. and Buckham, B. (2016). Nonlinear model predictive control for trajectory tracking of an AUV: A distributed implementation. Conference on Decision and Control, Las Vegas, NV, USA, 5998–6003.10.1109/CDC.2016.7799190Google Scholar
Sun, B., Zhu, D. and Yang, S.X. (2014). A bio-inspired filtered backstepping tracking control of 7000 m manned submarine vehicle. IEEE Transactions on Industrial Electronics, 61, 3682–3693.Google Scholar
Sun, B., Zhu, D. and Yang, S.X. (2016). A novel tracking controller for autonomous underwater vehicles with thruster fault accommodation. The Journal of Navigation, 69, 593–612.Google Scholar
Tsai, P. S., Wang, L. S. and Chang, F. R. (2004). Systematic backstepping design for b-spline trajectory tracking control of the mobile robot in hierarchical model. IEEE International Conference on Networking, Sensing and Control, Taipei, IEEE, 713–718.Google Scholar
Wu, Y. J., Zuo, J. X. and Sun, L. H. (2017). Adaptive terminal sliding mode control for hypersonic flight vehicles with strictly lower convex function based nonlinear disturbance observer. ISA transactions, 71, 215–226.Google Scholar
Xiang, X, Lapierre, L. and Jouvencel, B. (2015). Smooth transition of AUV motion control: from fully-actuated to under-actuated configuration. Robotics and Autonomous Systems, 67, 14–22.Google Scholar
Xiang, X., Yu, C. and Zhang, Q. (2017). On intelligent risk analysis and critical decision of underwater robotic vehicle. Ocean Engineering, 140, 453–465, Aug. 2017.Google Scholar
Xiang, X., Yu, C., Lapierre, L., Zhang, J. and Zhang, Q. (2018). Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. International Journal of Fuzzy Systems, 20, 572–586.Google Scholar
Yang, S. X. and Luo, C. (2004). A neural network approach to complete coverage path planning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34, 718–724.Google Scholar
Ye, J. (2014). Tracking control of a non-holonomic wheeled mobile robot using improved compound cosine function neural networks. International Journal of Control, 88, 364–373.Google Scholar
Yoon, Y., Shin, J., Kim, H. J., Park, Y. and Sastry, S. (2009). Model-predictive active steering and obstacle avoidance for autonomous ground vehicles. Control Engineering Practice, 17, 741–750.Google Scholar
Zhu, Z., Cao, X., Sun, B. and Luo, C. (2018) Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path planning of an AUV System. IEEE Transactions on Cognitive and Developmental Systems, 10, 304–313.Google Scholar