Adaptive fuzzy control for a marine vessel with time‐varying constraints

An adaptive fuzzy neural network (FNN) control scheme is proposed for a marine vessel with time-varying constraints, guaranteed transient response and unknown dynamics. A series of continuous constraint functions are introduced to shape the motion of a marine vessel. To deal with the constraint problems and transient response problems, an asymmetric time-varying barrier Lyapunov function is designed to ensure that the system states are upper bounded by the considered constraint functions. FNNs are constructed to identify the unknown dynamics. Considering existing approximation errors when FNNs approximating the unknown dynamics, an adaptive term is designed to compensate the approximation errors in order to obtain accurate control. Via Lyapunov stability theory, it has been proved that all the states in the closed-loop system are uniformly bounded ultimately without violating the corresponding prescribed constraint region. Two comparative simulations are carried out to verify the effectiveness of the proposed control.

[1]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[2]  Yang Li,et al.  Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Shuzhi Sam Ge,et al.  Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer , 2013, IEEE Transactions on Cybernetics.

[4]  Cong Wang,et al.  Neural Learning Control of Marine Surface Vessels With Guaranteed Transient Tracking Performance , 2016, IEEE Transactions on Industrial Electronics.

[5]  Kok Kiong Tan,et al.  Further results on adaptive control for a class of nonlinear systems using neural networks , 2003, IEEE Trans. Neural Networks.

[6]  Keng Peng Tee,et al.  Control of fully actuated ocean surface vessels using a class of feedforward approximators , 2006, IEEE Transactions on Control Systems Technology.

[7]  Chengyong Si,et al.  A Grouping Particle Swarm Optimizer with Personal-Best-Position Guidance for Large Scale Optimization , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[8]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[9]  Meng Joo Er,et al.  Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System , 2016, IEEE Transactions on Cybernetics.

[10]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Changyin Sun,et al.  Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer , 2017, IEEE Transactions on Cybernetics.

[12]  Chenguang Yang,et al.  Neural-Learning-Based Telerobot Control With Guaranteed Performance , 2017, IEEE Transactions on Cybernetics.

[13]  Shaocheng Tong,et al.  Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones , 2015, IEEE Transactions on Fuzzy Systems.

[14]  Alan F. Lynch,et al.  Inner–Outer Loop Control for Quadrotor UAVs With Input and State Constraints , 2016, IEEE Transactions on Control Systems Technology.

[15]  Gang Sun,et al.  Distributed Neural Network Control for Adaptive Synchronization of Uncertain Dynamical Multiagent Systems , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Ajith Abraham,et al.  A Trajectory Tracking Robust Controller of Surface Vessels With Disturbance Uncertainties , 2014, IEEE Transactions on Control Systems Technology.

[17]  Dong Yue,et al.  Control Synthesis of Discrete-Time T–S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach , 2016, IEEE Transactions on Cybernetics.

[18]  Tingwen Huang,et al.  Off-Policy Reinforcement Learning for $ H_\infty $ Control Design , 2013, IEEE Transactions on Cybernetics.

[19]  Jun Shi,et al.  Adaptive neural network control of a flexible string system with non-symmetric dead-zone and output constraint , 2017, Neurocomputing.

[20]  Chun-Yi Su,et al.  Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision , 2017, IEEE Transactions on Industrial Informatics.

[21]  Guang-Ren Duan,et al.  Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays , 2013, IEEE Transactions on Fuzzy Systems.

[22]  S. C. Tong,et al.  Adaptive Neural Network Decentralized Backstepping Output-Feedback Control for Nonlinear Large-Scale Systems With Time Delays , 2011, IEEE Transactions on Neural Networks.

[23]  Derong Liu,et al.  An Approximate Optimal Control Approach for Robust Stabilization of a Class of Discrete-Time Nonlinear Systems With Uncertainties , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Lu Bai,et al.  Adaptive Neural Control of Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems with Output Constraint and Unknown Dead Zone , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[25]  Yaohong Qu,et al.  Trajectory exponential tracking control of unmanned surface ships with external disturbance and system uncertainties. , 2018, ISA transactions.

[26]  Shuzhi Sam Ge,et al.  Neural Network Control of a Rehabilitation Robot by State and Output Feedback , 2015, J. Intell. Robotic Syst..

[27]  Ning Wang,et al.  Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles , 2016, IEEE Transactions on Control Systems Technology.

[28]  Marios M. Polycarpou,et al.  Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..

[29]  Keng Peng Tee,et al.  Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function , 2010, IEEE Transactions on Neural Networks.

[30]  Shuzhi Sam Ge,et al.  Vibration Control of a Flexible String With Both Boundary Input and Output Constraints , 2015, IEEE Transactions on Control Systems Technology.

[31]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Control of a Fully Actuated Marine Surface Vessel With Multiple Output Constraints , 2014, IEEE Transactions on Control Systems Technology.

[33]  Fuchun Sun,et al.  Decentralized Fuzzy Control of Multiple Cooperating Robotic Manipulators With Impedance Interaction , 2015, IEEE Transactions on Fuzzy Systems.

[34]  Shaocheng Tong,et al.  Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems , 2017, Autom..

[35]  Dong Yue,et al.  Adaptive neural tracking control of a class of MIMO pure-feedback time-delay nonlinear systems with input saturation , 2016, Int. J. Syst. Sci..

[36]  Meng Joo Er,et al.  Direct Adaptive Fuzzy Tracking Control of Marine Vehicles With Fully Unknown Parametric Dynamics and Uncertainties , 2016, IEEE Transactions on Control Systems Technology.

[37]  Yang Yang,et al.  Trajectory tracking control of nonlinear full actuated ship with disturbances , 2011, 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[38]  Shuzhi Sam Ge,et al.  Robust Adaptive Position Mooring Control for Marine Vessels , 2013, IEEE Transactions on Control Systems Technology.

[39]  S. Ge,et al.  Control of nonlinear systems with time-varying output constraints , 2009, IEEE International Conference on Control and Automation.

[40]  Yongduan Song,et al.  Tracking Control for a Class of Unknown Nonsquare MIMO Nonaffine Systems: A Deep-Rooted Information Based Robust Adaptive Approach , 2016, IEEE Transactions on Automatic Control.

[41]  Xiaogang Wang,et al.  Neural network based boundary control of a vibrating string system with input deadzone , 2018, Neurocomputing.

[42]  Zhongke Shi,et al.  Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[43]  Shaocheng Tong,et al.  Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[44]  Cong Wang,et al.  Dynamic Learning From Adaptive Neural Network Control of a Class of Nonaffine Nonlinear Systems , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[45]  C. L. Philip Chen,et al.  Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[46]  Wei He,et al.  Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.