Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation

In this paper, an uncertainty observation-based adaptive fuzzy neural dynamic surface control (UOB-AFNDSC) is proposed to investigate the problem of high-accuracy trajectory tracking of fully actuated underwater vehicles with respect to unknown uncertainties and input saturation. The control framework of UOB-AFNDSC is constructed using the dynamic surface technique, under which an online-succinct fuzzy-neuro uncertainty observer with both projection-based parameter learning and succinct network structure learning is constructed to online identify the lumped uncertainty term including system uncertainties and external disturbances. To further suppress the effect of uncertainty reconstruction error and input saturation error, two adaptive robust terms are introduced, respectively. To theoretically analyze the stability of the overall closed-loop control system, novel error variables are introduced to ensure the uniform ultimate boundedness of all signals, and the tracking accuracy can be easily adjusted by the width parameter of novel error variables. Finally, some simulations are carried out to demonstrate the effectiveness of the proposed control scheme.

[1]  Guoqing Xia,et al.  Fuzzy neural network-based robust adaptive control for dynamic positioning of underwater vehicles with input dead-zone , 2015, J. Intell. Fuzzy Syst..

[2]  Yongming Li,et al.  Observer-Based Adaptive Fuzzy Backstepping Dynamic Surface Control for a Class of MIMO Nonlinear Systems , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Yunpeng Zhang,et al.  Multivariable Finite Time Attitude Control for Quadrotor UAV: Theory and Experimentation , 2018, IEEE Transactions on Industrial Electronics.

[4]  Hamid Reza Karimi,et al.  Accurate Trajectory Tracking of Disturbed Surface Vehicles: A Finite-Time Control Approach , 2019, IEEE/ASME Transactions on Mechatronics.

[5]  Andrea Serrani,et al.  Global robust tracking with disturbance attenuation for unmanned underwater vehicles , 1998, Proceedings of the 1998 IEEE International Conference on Control Applications (Cat. No.98CH36104).

[6]  Xinxiang Pan,et al.  Path Following of Autonomous Underactuated Ships: A Translation–Rotation Cascade Control Approach , 2019, IEEE/ASME Transactions on Mechatronics.

[7]  Do Wan Kim,et al.  Tracking of REMUS autonomous underwater vehicles with actuator saturations , 2015, Autom..

[8]  Siyuan Liu,et al.  Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics , 2016, Neurocomputing.

[9]  Juan Li,et al.  Dynamic Surface and Active Disturbance Rejection Control for Path Following of an Underactuated UUV , 2014, J. Appl. Math..

[10]  Ying Ju,et al.  Solving Multidimensional 0-1 Knapsack Problem with Time-Free Tissue P Systems , 2014, J. Appl. Math..

[11]  Hamid Reza Karimi,et al.  Successive Waypoints Tracking of an Underactuated Surface Vehicle , 2020, IEEE Transactions on Industrial Informatics.

[12]  Mingjun Zhang,et al.  Adaptive sliding mode control based on local recurrent neural networks for underwater robot , 2012 .

[13]  Jin Bae Park,et al.  Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Wen Yang,et al.  Fixed-Time Leader–Follower Output Feedback Consensus for Second-Order Multiagent Systems , 2019, IEEE Transactions on Cybernetics.

[15]  Zhu Qidan,et al.  Path following control of fully-actuated autonomous underwater vehicle in presence of fast-varying disturbances , 2019, Applied Ocean Research.

[16]  Hong Wang,et al.  A fixed-time output feedback control scheme for double integrator systems , 2017, Autom..

[17]  Hamid Reza Karimi,et al.  Adaptive NN Dynamic Surface Controller Design for Nonlinear Pure-Feedback Switched Systems With Time-Delays and Quantized Input , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Alireza Khosravi,et al.  Adaptive $$\mu $$μ-modification control for a nonlinear autonomous underwater vehicle in the presence of actuator saturation , 2017 .

[19]  Eric Rogers,et al.  Design and Experimental Validation of an Adaptive Sliding Mode Observer-Based Fault-Tolerant Control for Underwater Vehicles , 2019, IEEE Transactions on Control Systems Technology.

[20]  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.

[21]  Ning Wang,et al.  Nonlinear disturbance observer-based backstepping finite-time sliding mode tracking control of underwater vehicles with system uncertainties and external disturbances , 2017 .

[22]  X. Liu,et al.  Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Qing-Long Han,et al.  Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization , 2019, IEEE Transactions on Industrial Electronics.

[24]  Edwin Kreuzer,et al.  An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles , 2010, Robotics Auton. Syst..

[25]  Lu Li,et al.  Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Guangming Xie,et al.  Yaw-Guided Trajectory Tracking Control of an Asymmetric Underactuated Surface Vehicle , 2019, IEEE Transactions on Industrial Informatics.

[27]  D. A. Schoenwald,et al.  AUVs: In space, air, water, and on the ground , 2000 .

[28]  Chaio-Shiung Chen.  Dynamic Structure Neural-Fuzzy Networks for Robust Adaptive Control of Robot Manipulators , 2008, IEEE Transactions on Industrial Electronics.

[29]  Dan Wang,et al.  Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form , 2005, IEEE Transactions on Neural Networks.

[30]  Nilanjan Sarkar,et al.  Adaptive control of an autonomous underwater vehicle: experimental results on ODIN , 2001, IEEE Trans. Control. Syst. Technol..

[31]  Petros A. Ioannou,et al.  Multiple Model Adaptive Control With Mixing , 2010, IEEE Transactions on Automatic Control.

[32]  Hongjing Liang,et al.  Adaptive Event-Triggered Output Feedback Fuzzy Control for Nonlinear Networked Systems With Packet Dropouts and Actuator Failure , 2019, IEEE Transactions on Fuzzy Systems.

[33]  Swaroop Darbha,et al.  Dynamic surface control for a class of nonlinear systems , 2000, IEEE Trans. Autom. Control..

[34]  Meng Joo Er,et al.  Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems , 2003, IEEE Trans. Fuzzy Syst..

[35]  Guangming Xie,et al.  Full-State Regulation Control of Asymmetric Underactuated Surface Vehicles , 2019, IEEE Transactions on Industrial Electronics.

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

[37]  Xin Zhang,et al.  Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities , 2016 .

[38]  Gianluca Antonelli,et al.  A novel adaptive control law for underwater vehicles , 2003, IEEE Trans. Control. Syst. Technol..

[39]  Yan Yan,et al.  Sliding mode tracking control of autonomous underwater vehicles with the effect of quantization , 2018 .

[40]  Choon Ki Ahn,et al.  Improved Stability Criteria for Discrete-Time Switched T–S Fuzzy Systems , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[41]  Zhouhua Peng,et al.  Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[42]  Guangjie Han,et al.  Surge-Heading Guidance-Based Finite-Time Path Following of Underactuated Marine Vehicles , 2019, IEEE Transactions on Vehicular Technology.

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

[44]  Ning Wang,et al.  Finite-Time Fault Estimator Based Fault-Tolerance Control for a Surface Vehicle With Input Saturations , 2020, IEEE Transactions on Industrial Informatics.

[45]  Ning Wang,et al.  Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with system uncertainties and unknown dead-zone nonlinearity , 2017 .

[46]  Chaio-Shiung Chen,et al.  Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems , 2011, IEEE Transactions on Fuzzy Systems.

[47]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[48]  Wei-Shou Chan,et al.  Adaptive Fuzzy Dynamic Surface Control for Ball and Beam System , 2011 .

[49]  Changchun Hua,et al.  Trajectory Tracking Control of Autonomous Underwater Vehicle With Unknown Parameters and External Disturbances , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[50]  Vincent Creuze,et al.  Observation-Based Nonlinear Proportional–Derivative Control for Robust Trajectory Tracking for Autonomous Underwater Vehicles , 2020, IEEE Journal of Oceanic Engineering.