scholarly journals Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jiemei Zhao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Path Tracking ◽  
Input Delay ◽  
Hydrodynamic Coefficients ◽  
Lyapunov Theorem ◽  
Neural Network Predictive Control

A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

Neural Network Predictive Control for Piezoelectric Smart Structures

2008 ◽  
Author(s):  
Jingjun Zhang ◽  
Ercheng Wang ◽  
Ruizhen Gao
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Control System ◽  
Predictive Control ◽  
Control Algorithm ◽  
Smart Structure ◽  
Generalized Predictive Control ◽  
The Neural Network ◽  
Nonlinear Generalized Predictive Control ◽  
Neural Network Predictive Control

The piezoelectric smart structure is a force-electric coupling structure, and piezoelectric patches can not be patched ideally, so it is difficult to build the accurate mathematical model of piezoelectric smart structure. The traditional vibration control methods depend on the structural mathematical model, and the control result is unsatisfactory. Considering this problem, this paper introduces the nonlinear generalized predictive control algorithm based on neural network predictive model into piezoelectric smart structure. Because of the difficulties of building the mathematical model and extracting dynamic data from experiment, the finite element software (ANSYS) is employed to analyze and obtain the dynamic response data of piezoelectric smart structure through modal analysis and transient analysis. Neural network predictive model of structure is built through off-line training on the basis of the data. The nonlinear generalized predictive control based on neural network has a better ability to solve complex nonlinear problem. Then the author introduces the Neural Network Based System Identification Toolbox (NNSYSID) and Neural Network Based Control System Design Toolkit (NNCTRL), which are two special toolboxes for designing neural network control system and can save lots of time for designers who can commit themselves to sixty-four-dollar question. At last, the author shows the method through a case. A cantilever beam which surface is boned piezoelectric patches used for sensor and actuator respectively is analyzed by ANSYS and controled by the neural network predictive control algorithm on the platform of NNSYSID and NNCTRL. This is a simple and effective method for designers to solve the vibration control problem of piezoelectric smart structure.

ChemInform Abstract: Thermal Stress Management of a Solid Oxide Fuel Cell Using Neural Network Predictive Control

ChemInform ◽  
2014 ◽  
Vol 45 (30) ◽  
pp. no-no
Author(s):  
S. A. Hajimolana ◽  
S. M. Tonekabonimoghadam ◽  
M. A. Hussain ◽  
M. H. Chakrabarti ◽  
N. S. Jayakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Stress ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Stress Management ◽  
Predictive Control ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Neural Network Predictive Control

Robust Adaptive Tracking Control of Autonomous Underwater Vehicle-Manipulator Systems

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Mohan Santhakumar ◽  
Jinwhan Kim
Keyword(s):  
Adaptive Control ◽  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Dynamic Coupling ◽  
Unknown Parameters ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Robust Adaptive ◽  
Model Reference Adaptive

This paper proposes a new tracking controller for autonomous underwater vehicle-manipulator systems (UVMSs) using the concept of model reference adaptive control. It also addresses the detailed modeling and simulation of the dynamic coupling between an autonomous underwater vehicle and manipulator system based on Newton–Euler formulation scheme. The proposed adaptation control algorithm is used to estimate the unknown parameters online and compensate for the rest of the system dynamics. Specifically, the influence of the unknown manipulator mass on the control performance is indirectly captured by means of the adaptive control scheme. The effectiveness and robustness of the proposed control scheme are demonstrated using numerical simulations.

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