Model Predictive Control for Tracking of Underactuated Vessels Based on Recurrent Neural Networks

In this paper, a model predictive control (MPC) scheme is presented for tracking of underactuated vessels with only two available controls: namely, surge force and yaw moment. When no external disturbance is explicitly considered, the proposed MPC approach iteratively solves a formulated quadratic programming (QP) problem using a single-layer recurrent neural network called the general projection network over a finite receding horizon. When additive disturbances are taken into account, a reformulated minimax optimization problem is iteratively solved by using a two-layer recurrent neural network. The applied neural networks are both stable in the sense of Lyapunov and globally convergent to the exact optimal solutions of reformulated convex programming problems. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed neurodynamics-based MPC approaches to vessel tracking control.

[1]  Eduardo F. Camacho,et al.  Neural network for constrained predictive control , 1993 .

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

[3]  Naomi Ehrich Leonard.  Control synthesis and adaptation for an underactuated autonomous underwater vehicle , 1995 .

[4]  Kristin Y. Pettersen,et al.  Exponential stabilization of an underactuated surface vessel , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[5]  F. Allgöwer,et al.  A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability , 1997 .

[6]  James B. Rawlings,et al.  Constrained linear quadratic regulation , 1998, IEEE Trans. Autom. Control..

[7]  Zhong-Ping Jiang,et al.  A recursive technique for tracking control of nonholonomic systems in chained form , 1999, IEEE Trans. Autom. Control..

[8]  Thor I. Fossen,et al.  Passive nonlinear observer design for ships using Lyapunov methods: full-scale experiments with a supply vessel , 1999, Autom..

[9]  James B. Rawlings,et al.  Tutorial overview of model predictive control , 2000 .

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

[11]  James A. Primbs,et al.  Feasibility and stability of constrained finite receding horizon control , 2000, Autom..

[12]  F. Fontes.  A General Framework to Design Stabilizing Nonlinear Model Predictive Controllers , 2001 .

[13]  H. Nijmeijer,et al.  Underactuated ship tracking control: Theory and experiments , 2001 .

[14]  Feng Wan,et al.  Structured neural networks for constrained model predictive control , 2001, Autom..

[15]  Riccardo Scattolini,et al.  A stabilizing model-based predictive control algorithm for nonlinear systems , 2001, Autom..

[16]  Khac Duc Do,et al.  Underactuated ship global tracking under relaxed conditions , 2002, IEEE Trans. Autom. Control..

[17]  Zhong-Ping Jiang,et al.  Global tracking control of underactuated ships by Lyapunov's direct method , 2002, Autom..

[18]  Zhong-Ping Jiang,et al.  Universal controllers for stabilization and tracking of underactuated ships , 2002, Syst. Control. Lett..

[19]  Kristin Ytterstad Pettersen,et al.  Global uniform asymptotic stabilization of an underactuated surface vessel , 2002, IEEE Trans. Autom. Control..

[20]  Jun Wang,et al.  A projection neural network and its application to constrained optimization problems , 2002 .

[21]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[22]  Kristin Ytterstad Pettersen,et al.  Tracking control of an underactuated ship , 2003, IEEE Trans. Control. Syst. Technol..

[23]  Jun Wang,et al.  A general projection neural network for solving monotone variational inequalities and related optimization problems , 2004, IEEE Transactions on Neural Networks.

[24]  Jun Wang,et al.  A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations , 2004, Neural Networks.

[25]  Youshen Xia,et al.  A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints , 2004, IEEE Trans. Circuits Syst. I Regul. Pap..

[26]  Khac Duc Do,et al.  State- and output-feedback robust path-following controllers for underactuated ships using Serret–Frenet frame , 2004 .

[27]  John Hauser,et al.  On the stability of receding horizon control with a general terminal cost , 2005, IEEE Transactions on Automatic Control.

[28]  Mauro Forti,et al.  Convergence of Neural Networks for Programming Problems via a Nonsmooth Łojasiewicz Inequality , 2006, IEEE Transactions on Neural Networks.

[29]  C.J. Ong,et al.  Enlarging the terminal region of nonlinear model predictive control using the support vector machine method , 2006, Autom..

[30]  Shubao Liu,et al.  A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application , 2006, IEEE Transactions on Neural Networks.

[31]  João P. Hespanha,et al.  Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty , 2007, IEEE Transactions on Automatic Control.

[32]  Lionel Lapierre,et al.  Nonlinear Path Following Control of an AUV , 2007 .

[33]  Long Cheng,et al.  Constrained multi-variable generalized predictive control using a dual neural network , 2007, Neural Computing and Applications.

[34]  Xiaolin Hu,et al.  An Improved Dual Neural Network for Solving a Class of Quadratic Programming Problems and Its $k$-Winners-Take-All Application , 2008, IEEE Transactions on Neural Networks.

[35]  Long Cheng,et al.  A Neutral-Type Delayed Projection Neural Network for Solving Nonlinear Variational Inequalities , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[36]  B. Jouvencel,et al.  Robust Nonlinear Path-Following Control of an AUV , 2008, IEEE Journal of Oceanic Engineering.

[37]  Jun Wang,et al.  Two neural network approaches to model predictive control , 2008, 2008 American Control Conference.

[38]  Qingshan Liu,et al.  A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming , 2008, IEEE Transactions on Neural Networks.

[39]  Long Cheng,et al.  A Delayed Projection Neural Network for Solving Linear Variational Inequalities , 2009, IEEE Transactions on Neural Networks.

[40]  Jing Sun,et al.  Path following of underactuated marine surface vessels using line-of-sight based model predictive control ☆ , 2010 .

[41]  Zao-jian Zou,et al.  Path following control of underactuated ships based on nonswitch analytic model predictive control , 2010 .

[42]  M. Soroush,et al.  Model Predictive Control Tuning Methods: A Review , 2010 .

[43]  Long Cheng,et al.  Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks , 2011, IEEE Transactions on Neural Networks.

[44]  Lars Grne,et al.  Nonlinear Model Predictive Control: Theory and Algorithms , 2011 .

[45]  Jun Wang,et al.  Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network , 2011, The 2011 International Joint Conference on Neural Networks.

[46]  Jun Wang,et al.  A One-Layer Recurrent Neural Network for Constrained Nonsmooth Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[47]  Jun Wang,et al.  Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks , 2012, IEEE Transactions on Industrial Electronics.