scholarly journals Oscillatory Adaptive Yaw-Plane Control of Biorobotic Autonomous Underwater Vehicles Using Pectoral-Like Fins

2007 ◽  
Vol 4 (4) ◽  
pp. 137-147 ◽  
Author(s):  
Mugdha S. Naik ◽  
Sahjendra N. Singh
Keyword(s):  
Control System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Angle Measurement ◽  
Trajectory Control ◽  
Physical Parameters ◽  
Sampled Data ◽  
Parameter Uncertainties ◽  
Marine Animals ◽  
Yaw Angle

This article considers the control of a biorobotic autonomous underwater vehicle (BAUV) in the yaw plane using biologically inspired oscillatory pectoral-like fins of marine animals. The fins are assumed to be oscillating harmonically with a combined linear (sway) and angular (yaw) motion producing unsteady forces, which are used for fish-like control of BAUVs. Manoeuvring of the BAUV in the yaw plane is accomplished by altering the bias (mean) angle of the angular motion of the fin. For the derivation of the adaptive control system, it is assumed that the physical parameters, the hydrodynamic coefficients, and the fin force and moment are not known. A direct adaptive sampled-data control system for the trajectory control of the yaw-angle using only yaw-angle measurement is derived. The parameter adaptation law is based on the normalised gradient scheme. Simulation results for the set point control, sinusoidal trajectory tracking and turning manoeuvres are presented, which show that the control system accomplishes precise trajectory control in spite of the parameter uncertainties.

scholarly journals Biology-Inspired Robust Dive Plane Control of Non-Linear AUV Using Pectoral-Like Fins

2010 ◽  
Vol 7 (2) ◽  
pp. 153-168
Author(s):  
Subramanian Ramasamy ◽  
Sahjendra N. Singh
Keyword(s):  
Control System ◽  
Internal Model ◽  
Autonomous Underwater Vehicle ◽  
Linear Time ◽  
Autonomous Underwater Vehicles ◽  
Tracking Error ◽  
Control Variable ◽  
Model Parameters ◽  
Marine Animals ◽  
Non Linear

The development of a control system for the dive plane control of non-linear biorobotic autonomous underwater vehicles, equipped with pectoral-like fins, is the subject of this paper. Marine animals use pectoral fins for swimming smoothly. The fins are assumed to be oscillating with a combined pitch and heave motion and therefore produce unsteady control forces. The objective is to control the depth of the vehicle. The mean angle of pitch motion of the fin is used as a control variable. A computational-fluid-dynamics-based parameterisation of the fin forces is used for control system design. A robust servo regulator for the control of the depth of the vehicle, based on the non-linear internal model principle, is derived. For the control law derivation, an exosystem of third order is introduced, and the non-linear time-varying biorobotic autonomous underwater vehicle model, including the fin forces, is represented as a non-linear autonomous system in an extended state space. The control system includes the internal model of ak-fold exosystem, wherekis a positive integer chosen by the designer. It is shown that in the closed-loop system, all the harmonic components of order up tokof the tracking error are suppressed. Simulation results are presented which show that the servo regulator accomplishes accurate depth control despite uncertainties in the model parameters.

Backstepping Control Method for the Trajectory Tracking for the Underactuated Autonomous Underwater Vehicle

2013 ◽  
Vol 798-799 ◽  
pp. 484-488 ◽  
Author(s):  
Lei Wan ◽  
Nan Sun ◽  
Yu Lei Liao
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Autonomous Underwater Vehicles ◽  
Path Following ◽  
Underactuated System ◽  
Strong Nonlinearity ◽  
Parameter Uncertainties ◽  
Path Following Control

The underactuated autonomous underwater vehicles (AUV) have the characteristics of strong nonlinearity and model uncertainty. A method of backstepping path following control was raised for the trajectory tracking control problem of the AUV under Serret-Frenet frame. It transformed the original underactuated system into an actuated nonlinear system based on simplified analysis. A backstepping trajectory tracking controller was proposed based on backstepping method. By means of Lyapunov stability theory, it was proven that the proposed controller can guarantee the path following control system globally asymptotically stable. Simulation experiments show that the control system has good adaptability and robustness in case of parameter uncertainties and external disturbances to avoid shaking of performance.

Safe Navigation Algorithm for Autonomous Underwater Vehicles

2021 ◽  
Vol 29 (1) ◽  
pp. 97-110
Author(s):  
V.S. Bykova ◽  
◽  
A.I. Mashoshin ◽  
I.V. Pashkevich ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Limited Size ◽  
Safe Navigation ◽  
Navigation Algorithm ◽  
Navigation Algorithms ◽  
Ice Edge

Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.

Robust Control Research for AUV Trajectory Control System

2011 ◽  
Vol 148-149 ◽  
pp. 93-96
Author(s):  
Juan Li ◽  
Xin Qian Bian ◽  
Hua Sheng Xiong ◽  
Hong Jian Wang
Keyword(s):  
Control System ◽  
Robust Control ◽  
Control Problem ◽  
Horizontal Plane ◽  
Vertical Plane ◽  
Trajectory Control ◽  
Parameter Uncertainties ◽  
External Disturbances ◽  
Heading Control ◽  
Control Research

A robust trajectory control problem for an underactuated AUV with parameter uncertainties and external disturbances was considered. The trajectory control was decomposed in the horizontal plane and vertical plane. Based on the robust theory, the AUV model for heading control was proposed, and the heading controller was designed. The simulated results show that the method can effectively overcome disturbances of constant ocean currents to ensure the precision track.

An Autonomous Underwater Vehicle for Competition

2013 ◽  
Vol 380-384 ◽  
pp. 595-600
Author(s):  
Hai Tian ◽  
Bo Hu ◽  
Can Yu Liu ◽  
Guo Chao Xie ◽  
Hui Min Luo
Keyword(s):  
Control System ◽  
Hydrodynamic Model ◽  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Vertical Motion ◽  
Underwater Vehicle ◽  
Step Response ◽  
Six Degrees Of Freedom ◽  
Simulink Simulation

The research of this paper was derived from the small autonomous underwater vehicle (AUV)Raider well performed in the 15th International Underwater Vehicle Competition (IAUVC),San Diego. In order to improve the performance of underwater vehicle, the control system of performance motion played an important role on autonomous underwater vehicles stable motion, and the whole control system of AUV is the main point. Firstly, based on the motion equations of six degrees of freedom, the paper simplified the dynamical model reasonably in allusion; Due to the speed of Raider to find the target was very low, this paper considered the speed was approximately zero and only considered the vertical motion. Therefore, this paper established the vertical hydrodynamic model of Raider, obtaining the transfer equation of vertical motion. Through the experiment and Matlab/Simulink simulation, this paper got the actual depth of the step response curve and simulation curve, and verified the validity of the vertical hydrodynamic model and the correlation coefficient.

Robust generalized dynamic inversion based control of autonomous underwater vehicles

2017 ◽  
Vol 232 (4) ◽  
pp. 434-447 ◽  
Author(s):  
Uzair Ansari ◽  
Abdulrahman H Bajodah
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Closed Loop ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Dynamic Inversion ◽  
Control Law ◽  
Closed Loop System

A novel two-loop structured robust generalized dynamic inversion–based control system is proposed for autonomous underwater vehicles. The outer (position) loop of the generalized dynamic inversion control system utilizes proportional-derivative control of the autonomous underwater vehicle’s inertial position errors from the desired inertial position trajectories, and it provides the reference yaw and pitch attitude angle commands to the inner loop. The inner (attitude) loop utilizes generalized dynamic inversion control of a prescribed asymptotically stable dynamics of the attitude angle errors from their reference values, and it provides the required control surface deflections such that the desired inertial position trajectories of the vehicle are tracked. The dynamic inversion singularity is avoided by augmenting a dynamic scaling factor within the Moore–Penrose generalized inverse in the particular part of the generalized dynamic inversion control law. The involved null control vector in the auxiliary part of the generalized dynamic inversion control law is constructed to be linear in the pitch and yaw angular velocities, and the proportionality gain matrix is designed to guarantee global closed-loop asymptotic stability of the vehicle’s angular velocity dynamics. An additional sliding mode control element is included in the particular part of the generalized dynamic inversion control system, and it works to robustify the closed-loop system against tracking performance deterioration due to generalized inversion scaling, such that semi-global practically stable attitude tracking is guaranteed. A detailed six degrees-of-freedom mathematical model of the Monterey Bay Aquarium Research Institute autonomous underwater vehicle is used to evaluate the control system design, and numerical simulations are conducted to demonstrate closed-loop system performance under various types of autonomous underwater vehicle maneuvers, under both nominal and perturbed autonomous underwater vehicle system’s mathematical model parameters.

scholarly journals Autonomous underwater vehicle precise motion control for target following with model uncertainty

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141771980 ◽  
Author(s):  
Huang Hai ◽  
Zhang Guocheng ◽  
Qing Hongde ◽  
Zhou Zexing
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
External Disturbance ◽  
Underwater Vehicle ◽  
Parameter Uncertainties ◽  
Fuzzy Neural ◽  
Recurrent Type

Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

scholarly journals Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Tao Liu ◽  
Yuli Hu ◽  
Hui Xu
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Continuous Control ◽  
Trial And Error ◽  
Simulation Environment ◽  
Reward Function ◽  
Underwater Vehicle Control ◽  
Control Accuracy

Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.

scholarly journals NAVIGATION AND MOTION CONTROL SYSTEMS OF THE AUTONOMOUS UNDERWATER VEHICLE

2020 ◽  
Vol 4 ◽  
pp. 38-50
Author(s):  
Dmitry Antonov ◽  
Leonid Kolganov ◽  
Aleksey Savkin ◽  
Egor Chekhov ◽  
Maxim Ryabinkin
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Control Systems ◽  
Motion Control ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Transportation Safety ◽  
Doppler Velocity ◽  
Using Data

Autonomous underwater vehicles (AUVs) are widely used and have proven their effectiveness in tasks such as transportation safety, area monitoring and seafloor mapping. When developing AUV’s navigation and control systems, the engineers have to ensure the required levels of accuracy and reliability for solving navigation and motion control tasks in autonomous underwater operation under restrictions on the overall dimensions and power consumption of the AUV. The main purpose of this paper is to present preliminary results of AUV navigation and motion control systems development. The AUV’s navigation system is built around strapdown inertial navigation system (SINS) designed specifically for this AUV. When surfaced, position and angular SINS correction is performed using data from dual-antenna GNSS receiver and doppler velocity log (DVL). When underwater, SINS position and velocity correction is performed using acoustic navigation system (ANS) and DVL data. AUV’s control system provides manual and automatic control. Manual control is carried out in real-time by operator via fiber-optic cable using a joystick. Automatic control allows AUV to move independently along a specified trajectory at a given depth and speed. The AUV also has a collision avoidance system that utilizes readings from a forward-facing acoustic rangefinder to estimate time before impact based on AUV’s analytic model. If possible collision is detected, information is transmitted to the control system so that a further appropriate action can be taken. Computer simulation utilizing the analytic AUV model was used in order to check the performance characteristics of the designed control and navigation algorithms. After confirming the operability of the developed algorithms, preliminary tests of the AUV were carried out. During the tests, AUV’s on-board equipment and navigation system readings were recorded and compared to the readings of the reference system, which was also installed on the AUV. During the tests, the dynamic characteristics of the AUV were evaluated. AUV’s characteristics obtained during simulation and testing will be used as a reference during future development

Homing and Docking Autopilot Design for Autonomous Underwater Vehicle

2014 ◽  
Vol 490-491 ◽  
pp. 700-707 ◽  
Author(s):  
V.K. Pshikhopov ◽  
M.Yu. Medvedev ◽  
B.V. Gurenko
Keyword(s):  
Control System ◽  
Dynamic Model ◽  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Underwater Vehicle ◽  
Trajectory Control

This paper presents homing and docking autopilot design for the autonomous underwater vehicle (AUV). A nonlinear interrelated dynamic model of the underwater vehicle is considered. The AUV autopilot is designed on base of a position-trajectory control method. Adaptation of the control system is based on robust disturbances estimation. Modeling and hardware results approved feasibility of the proposed methods.

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