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

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.

[1]  Chaomin Luo,et al.  Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path Planning of an AUV System , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[2]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[3]  Lionel Lapierre,et al.  Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles , 2018, Int. J. Fuzzy Syst..

[4]  Mingyue Cui,et al.  Adaptive Tracking and Obstacle Avoidance Control for Mobile Robots with Unknown Sliding , 2012 .

[5]  Paolo Falcone,et al.  Nonlinear Model Predictive Control for Autonomous Vehicles , 2007 .

[6]  Martin Horn,et al.  Certainty equivalence adaptation combined with super-twisting sliding-mode control , 2016, Int. J. Control.

[7]  Chao Shen,et al.  Nonlinear model predictive control for trajectory tracking of an AUV: A distributed implementation , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[8]  S. Shankar Sastry,et al.  Model-predictive active steering and obstacle avoidance for autonomous ground vehicles , 2009 .

[9]  Fan-Ren Chang,et al.  Systematic backstepping design for B-spline trajectory tracking control of the mobile robot in hierarchical model , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[10]  Yun-jie Wu,et al.  Adaptive terminal sliding mode control for hypersonic flight vehicles with strictly lower convex function based nonlinear disturbance observer. , 2017, ISA transactions.

[11]  Ray Eaton,et al.  Robust Model Predictive Control for automated trajectory tracking of an Unmanned Ground Vehicle , 2012, 2012 American Control Conference (ACC).

[12]  Simon X. Yang,et al.  An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics , 2013, Expert Syst. Appl..

[13]  Jun Ye,et al.  Tracking control of a non-holonomic wheeled mobile robot using improved compound cosine function neural networks , 2015, Int. J. Control.

[14]  Chaomin Luo,et al.  A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments , 2008, IEEE Transactions on Neural Networks.

[15]  Qin Zhang,et al.  On intelligent risk analysis and critical decision of underwater robotic vehicle , 2017 .

[16]  Bruno Jouvencel,et al.  Smooth transition of AUV motion control: From fully-actuated to under-actuated configuration , 2015, Robotics Auton. Syst..

[17]  S.X. Yang,et al.  A neural network approach to complete coverage path planning , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Simon X. Yang,et al.  A Bioinspired Filtered Backstepping Tracking Control of 7000-m Manned Submarine Vehicle , 2014, IEEE Transactions on Industrial Electronics.

[19]  Edwin Kreuzer,et al.  Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller , 2008, Robotics Auton. Syst..

[20]  S. Shankar Sastry,et al.  Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[21]  Tieshan Li,et al.  A DSC and MLP based robust adaptive NN tracking control for underwater vehicle , 2013, Neurocomputing.

[22]  Ron P. Podhorodeski,et al.  A chattering-free sliding-mode controller for underwater vehicles with fault- tolerant infinity-norm thrust allocation , 2008 .

[23]  Renquan Lu,et al.  Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Mohammad Farrokhi,et al.  Robust adaptive control of uncertain non-linear systems using neural networks , 2008, Int. J. Control.

[25]  Simon X. Yang,et al.  A Novel Tracking Controller for Autonomous Underwater Vehicles with Thruster Fault Accommodation , 2015, Journal of Navigation.

[26]  Zheping Yan,et al.  Three-dimensional Path Following Control for an Underactuated UUV Based on Nonlinear Iterative Sliding Mode: Three-dimensional Path Following Control for an Underactuated UUV Based on Nonlinear Iterative Sliding Mode , 2012 .

[27]  Ahmad Bagheri,et al.  Simulation and tracking control based on neural-network strategy and sliding-mode control for underwater remotely operated vehicle , 2009, Neurocomputing.