A Robust Reach Set MPC Scheme for Control of AUVs

A Robust Model Predictive Control (MPC) scheme for the control of formations of Autonomous Underwater Vehicles (AUVs) is presented and discussed. This application domain is extremely relevant and exhibits very difficult control challenges: (i) slow, low data-rate acoustic communications, (ii) significant perturbations inherent to the hydrodynamic environment, (iii) unexpected emergence of obstacles, and (iv) severe onboard computation constraints. While the later aspect is discussed by the Reach Set MPC scheme implementation which maximizes the a priori off-line computation as enabled by taking into account, as much as possible, invariant data, the other challenges are addressed by increasing the robustness of the proposed basic MPC scheme by considering a number of intermediate steps which, in spite of the increase of the on-line computational burden, this remains strongly lower than the one associated with typical standard MPC schemes.

[1]  B. Krogh,et al.  Hyperplane method for reachable state estimation for linear time-invariant systems , 1991 .

[2]  P. Varaiya,et al.  Dynamic Optimization for Reachability Problems , 2001 .

[3]  Sylvain Bertrand,et al.  MPC Strategies for Cooperative Guidance of Autonomous Vehicles , 2014 .

[4]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

[5]  Yu. S. Ledyaev,et al.  Nonsmooth analysis and control theory , 1998 .

[6]  Ye Sun,et al.  Stability analysis of discontinuous dynamical systems determined by semigroups , 2005, IEEE Transactions on Automatic Control.

[7]  A. Richards,et al.  Model predictive control of spacecraft formations with sensing noise , 2005, Proceedings of the 2005, American Control Conference, 2005..

[8]  Qinglei Hu,et al.  6 DOF synchronized control for spacecraft formation flying with input constraint and parameter uncertainties. , 2011, ISA transactions.

[9]  João Pedro Hespanha,et al.  Robust UAV coordination for target tracking using output-feedback model predictive control with moving horizon estimation , 2015, 2015 American Control Conference (ACC).

[10]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[11]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[12]  Per Johan Nicklasson,et al.  Spacecraft formation flying: A review and new results on state feedback control , 2009 .

[13]  Naomi Ehrich Leonard,et al.  Control of coordinated patterns for ocean sampling , 2007, Int. J. Control.

[14]  T. Parisini,et al.  Cooperative control of discrete-time agents with delayed information exchange: A receding-horizon approach , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[15]  Francesco Borrelli,et al.  Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations , 2008, IEEE Transactions on Control Systems Technology.

[16]  Timothy Prestero,et al.  Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicle , 2001 .

[17]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[18]  R. Beard,et al.  VIRTUAL STRUCTURE BASED SPACECRAFT FORMATION CONTROL WITH FORMATION FEEDBACK , 2002 .

[19]  Zhou Chao,et al.  Collision-free UAV formation flight control based on nonlinear MPC , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[20]  Pravin Varaiya,et al.  Reach Set Computation Using Optimal Control , 2000 .