Review on auto-depth control system for an unmanned underwater remotely operated vehicle (ROV) using intelligent controller

This paper presents a review of auto-depth control system for an Unmanned Underwater Remotely operated Vehicle (ROV), focusing on the Artificial Intelligent Controller Techniques. Specifically, Fuzzy Logic Controller (FLC) is utilized in auto-depth control system for the ROV. This review covered recently published documents for auto-depth control of an Unmanned Underwater Vehicle (UUV). This paper also describes the control issues in UUV especially for the ROV, which has inspired the authors to develop a new technique for auto-depth control of the ROV, called the SIFLC. This technique was the outcome of an investigation and tuning of two parameters, namely the break point and slope for the piecewise linear or slope for the linear approximation. Hardware comparison of the same concepts of ROV design was also discussed. The ROV design is for smallscale, open frame and lower speed. The review on auto-depth control system for ROV, provides insights for readers to design new techniques and algorithms for auto-depth control

Observer based output feedback tuning for underwater remotely operated vehicle based on linear quadratic performance

This paper describes the effectiveness of observer-based output feedback for Unmanned Underwater Vehicle (UUV) with Linear Quadratic Regulation (LQR) performance. Tuning of observer parameters is crucial for tracking purpose. Prior to tuning facility, the ranges of observer and LQR parameters are obtained via system output cum error. The validation of this technique using unmanned underwater vehicles called Remotely Operated Vehicle (ROV) modelling helps to improve steady state performance of system response. The ROV modeling is focused for depth control using ROV 1 developed by the Underwater Technology Research Group (UTeRG). The results are showing that this technique improves steady state performances in term of overshoot and settling time of the system response

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

Remotely Operated Vehicle also known as ROV is a vehicle with high nonlinearity and uncertainty parameters that requires a robust control system to maintain stability. The nonlinearity and uncertainty of ROV are caused by underwater environmental conditions and by the movement of the vehicle. SMC is one of the control systems that can overcome nonlinearity and uncertainty with the given robust system. This work aims to control velocity of the vehicle with proposes the use of modified integral SMC compensate error in ROV and the use of particle swarm optimization (PSO) to optimize the adjustment of SMC parameters. The ROV used in this paper has a configuration of six thrusters with five DoF movements that can be controlled. Modified integral sliding mode is used to control all force direction to increase the convergence of speed error. Adjustment optimization techniques with PSO are used to determine four values of sliding control parameters for five DoF. Using Lyapunov stability approach control law of sliding mode is derived and its global stability proved mathematically. Simulation results are conducted to evaluate the effectiveness of Modified Integral SMC and compared with nonlinear control

Depth control of an underwater remotely operated vehicle using neural network predictive control

This paper investigates the depth control of an unmanned underwater remotely operated vehicle (ROV) using neural network predictive control (NNPC). The NNPC is applied to control the depth of the ROV to improve the performances of system response in terms of overshoot. To assess the viability of the method, the system was simulated using MATLAB/Simulink by neural network predictive control toolbox. In this paper also investigates the number of data samples (1000, 5000 and 10,000) to train neural network. The simulation reveals that the NNPC has the better performance in terms of its response, but the execution time will be increased. The comparison between other controller such as conventional PI controller, Linear Quadratic Regulation (LQR) and fuzzy logic controller also covered in this paper where the main advantage of NNPC is the fastest system response on depth control

Depth control of an underwater remotely operated vehicle using neural network predictive control

This paper investigates the depth control of an unmanned underwater remotely operated vehicle (ROV) using neural network predictive control (NNPC). The NNPC is applied to control the depth of the ROV to improve the performances of system response in terms of overshoot. To assess the viability of the method, the system was simulated using MATLAB/Simulink by neural network predictive control toolbox. In this paper also investigates the number of data samples (1000, 5000 and 10,000) to train neural network. The simulation reveals that the NNPC has the better performance in terms of its response, but the execution time will be increased. The comparison between other controller such as conventional PI controller, Linear Quadratic Regulation (LQR) and fuzzy logic controller also covered in this paper where the main advantage of NNPC is the fastest system response on depth control

A Comparison Study Between Two Algorithms Particle Swarm Optimization for Depth Control of Underwater Remotely Operated Vehicle

This paper investigates two algorithms based on particle swarm optimization (PSO) to obtain optimum parameter. In this research, an improved PSO algorithm using a priority-based fitness PSO (PFPSO) and priority-based fitness binary PSO (PFBPSO) approach. This comparison study between two algorithms applied on underwater Remotely Operated Vehicle for depth control. Two parameters in Single Input Fuzzy Logic Controller will tune using two algorithms to obtain optimum parameter. There are two parameters to be tuned namely the break point and slope for the piecewise linear or slope for the linear approximation. The study also covered a comparison for time execution for every time the parameter tuning was done. Based on the results the PFBPSO gives a consistent value of optimum parameter and time execution very fast. The best optimum parameter of SIFLC determined using 2 methods such that average of optimum parameter and intersection of y-axis. The PFBPSO gives comparative results in term of two parameters and time execution very fast compared with improved PSO

Hovering-mode control of the glider-type unmanned underwater vehicle

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2011Includes bibliographical references (leaves: 104-107)Text in English; Abstract: Turkish and Englishxiii, 109 leavesResearch on the underwater robotics has attracted the interest of many researchers over the years. The primary reasons are the need to perform underwater intervention tasks that are dangerous for a diver and the need to perform underwater survey tasks that last for longer periods of time. Unmanned underwater vehicles can be divided into two categories. Most of the systems, today, that require a certain level of precision and dexterity are built as Remotely Operated Vehicles (ROV). On the other hand, the systems that perform repetitive tasks are configured as Autonomous Underwater Vehicles (AUV). The objective of the thesis is to design a novel, cost-efficient, and fault-tolerant ROV that can hover and be used for shallow water investigation. In order to reduce the cost, the numbers of thrusters are minimized and internal actuators are used for steering the vehicle and stability in hovering mode. Also, the design is planned to be open for modification for further improvements that will enable the use of the vehicle for intervention tasks and studies. In this work, previously developed unmanned underwater vehicles are reviewed. Following this, the conceptual designs are created for the underwater vehicle and internal actuator designs are developed. Designed mechanisms are modeled in SolidWorks© and transferred to MATLAB© Simulink for hovering-mode control studies. Afterwards, to verify the simulation results, experiments are conducted with a seesaw mechanism by using LabVIEW© programming. Finally, results are given, discussed and future works are addressed

Adaptive simplified fuzzy logic controller for depth control of underwater remotely operated vehicle

A Remotely Operated Vehicle (ROV) is one class of the unmanned underwater vehicles that is tethered, unoccupied, highly manoeuvrable, and operated by a person on a platform on water surface. For depth control of ROV, an occurrence of overshoot in the system response is highly dangerous. Clearly an overshoot in the ROV vertical trajectory may cause damages to both the ROV and the inspected structure. Maintaining the position of a small scale ROV within its working area is difficult even for experienced ROV pilots, especially in the presence of underwater currents and waves. This project, focuses on controlling the ROV vertical trajectory as the ROV tries to remain stationary on the desired depth and having its overshoot, rise time and settling time minimized. This project begins with a mathematical and empirical modelling to capture the dynamics of a newly fabricated ROV, followed by an intelligent controller design for depth control of ROV based on the Single Input Fuzzy Logic Controller (SIFLC). Factors affecting the SIFLC were investigated including changing the number of rules, using a linear equation instead of a lookup table and adding a reference model. The parameters of the SIFLC were tuned by an improved Particle Swarm Optimization (PSO) algorithm. A novel adaptive technique called the Adaptive Single Input Fuzzy Logic Controller (ASIFLC) was introduced that has the ability to adapt its parameters depending on the depth set point used. The algorithm was verified in MATLAB® Simulink platform. Then, verified algorithms were tested on an actual prototype ROV in a water tank. Results show it was found that the technique can effectively control the depth of ROV with no overshoot and having its settling time minimized. Since the algorithm can be represented using simple mathematical equations, it can easily be realized using low cost microcontrollers

Fault Diagnosis Techniques for Linear Sampled Data Systems and a Class of Nonlinear Systems

This thesis deals with the fault diagnosis design problem both for dynamical continuous time systems whose output signal are affected by fixed point quantization,\ud referred as sampled-data systems, and for two different applications whose dynamics are inherent high nonlinear: a remotely operated underwater vehicle and a scramjet-powered hypersonic vehicle.\ud Robustness is a crucial issue. In sampled-data systems, full decoupling of disturbance terms from faulty signals becomes more difficult after discretization.\ud In nonlinear processes, due to hard nonlinearity or the inefficiency of linearization, the “classical” linear fault detection and isolation and fault tolerant control methods may not be applied.\ud Some observer-based fault detection and fault tolerant control techniques are studied throughout the thesis, and the effectiveness of such methods are validated with simulations. The most challenging trade-off is to increase sensitivity to faults and robustness to other unknown inputs, like disturbances. Broadly speaking, fault detection filters are designed in order to generate analytical diagnosis functions, called residuals, which should be independent with respect to the system operating state and should be decoupled from disturbances. Decisions on the occurrence of a possible fault are therefore taken on the basis such residual signals