Development of Subsea Altimeter Sensor System (SASS) Using Portable Sonar Sensor Fish Finder Alarm for Unmanned Underwater Vehicles

This paper describes the development of Subsea Altimeter Sensor System (SASS) for Unmanned Underwater Vehicles (UUV) Application using portable sonar sensor fish finder alarm system. Altimeter Sensor system is used to measure the depth of water. This altimeter sensor design valid for shallow water depth ranges maximum 100 m. This SASS will be applied to Underwater Remotely Operated Vehicles (ROV) design to verify the SASS performances. Experiments conducted to measure a depth of lab test, swimming pool test and Ayer Keroh Lake test. The experiments conducted in lab pool and swimming pool to measure and estimate the error and accuracy of SASS performances because of known the depth of water. The error of Altimeter Sensor System is 10% or ± 5 cm depth and accuracy of SASS very high about 90% for the both experiments. The results on Lake of Ayer Keroh at certain point can be acceptable. The 3D design of seabed mapping is plotted using MATLAB and Excel

Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV)

In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an UUV is described in this paper. The main purpose of the underwater vehicle is that the vehicle must be stable over the entire range of operation. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth.The depth performance for NNPC is discussed in terms of error and execution time. This NNPC will be compared with conventional controller such as PD controller and also by using the Fuzzy Logic Controller (FLC). For the comparison of computational time between this controllers, it can be observed that Fuzzy Logic is faster and neural network predictive controller is the slowest between them. It has been shown that the neural network predictive controller improved the transient response and error measure which shows the effectiveness of the designed controller

Comparison of depth control form surface and bottom set point of an unmanned underwater remotely operated vehicle using PID controller

This paper investigates the depth control of an Unmanned Underwater Remotely Operated Vehicle (ROV) based on ballast tank system using conventional PID controller. The PID Controller is applied to control the depth of the ROV from two different reference points, from the surface and from the seafloor. The concept of ballast tank system selected is piston tank type. Two different sensors are selected, which is pressure sensor for measurement from the surface, and sonar sensor for measurement from the bottom. Control method from both references point are investigated and compared to find out which feedback reference points are more appropriate in different conditions. The implementation phase will be verified through MATLAB Simulink platform. The verified algorithms will then be tested on the actual prototype ROV. And also the prospect of automated the vertical movement of a ROV

Underwater Technology Research Group (UTeRG) Glider for Monitoring and Surveillances Applications

This paper describes a design and development of Underwater Glider for Monitoring and surveillances applications. An Underwater Glider is a type of Autonomous Underwater Vehicle (AUV). Underwater gliders are buoyancy-driven device. It can alternately reduce and expand displaced volume to dive or climb through the ocean. It has wings to control its motion from vertical to horizontal at very low power consumption. The motivation of this underwater glider is at its long range and high endurance for certain types of mission. Gliders are designed to slip through the ocean a fraction of meter per second to cover hundreds of meters for weeks. It can be used in commercial and military purpose. The design and development of this underwater glider have hydrodynamic characteristic, stability and buoyancy. The simple Microprocessor PIC is used to control the movement of the glider. There are three major phase in developing this glider which are mechanical design, programming and fabrication. The speed and power consumption of the glider in pool and lake are then measured and analyzed. This glider was tested on three types of differences testing area such as lake, swimming pool and laboratory pool. This paper also shows the performances of glider in term of speed and power consumption of three conditions. This glider is proven suitable for monitoring and surveillances application

Comparison of Depth Control from Surface and Bottom Set point of an Unmanned Underwater Remotely Operated Vehicle using PID Controller

This paper investigates the depth control of an Unmanned Underwater Remotely Operated Vehicle (ROV) based on ballast tank system using conventional PID controller. The PID Controller is applied to control the depth of the ROV from two different reference points, from the surface and from the seafloor. The concept of ballast tank system selected is piston tank type. Two different sensors are selected, which is pressure sensor for measurement from the surface, and sonar sensor for measurement from the bottom. Control method from both references point are investigated and compared to find out which feedback reference points are more appropriate in different conditions. The implementation phase will be verified through MATLAB Simulink platform. The verified algorithms will then be tested on the actual prototype ROV. And also the prospect of automated the vertical movement of a ROV

DEVELOPMENT AND MODELLING OF UNMANNED UNDERWATER GLIDER USING THE SYSTEM IDENTIFICATION METHOD

This paper describes a comparison study for the modelling of the unmanned Underwater Glider (UG) using system identification techniques based on two experimental set up. The experimental data obtained from lab tank test and pool test to infer model using a MATLAB System Identification toolbox. The experimental testing of UG only considered the horizontal movement or called as auto-heading. The modeling obtained will be used to design the suitable controller for heading control. The UG will be tested on an open loop system to obtain measured input-output signals. Input and output signals from the system are recorded and analyzed to infer a model using a System Identification MATLAB toolbox. Two models obtained based on data tabulated and verify using mathematical modelling of UG. The parameter of UG come up from the real model of UG and Solidworks software. The Underwater Lab Tank model has better performance which has faster rise time and settling time than swimming pool model and mathematical model

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

Direct Adaptive Predictive Control For Wastewater Treatment Plant

The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system-proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process

Depth control of an unmanned 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