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

Brand credibility, perceived quality and perceived value: a study of customer satisfaction

This research examines the relationship of brand credibility, perceived quality and perceived value towards customer satisfaction and to what extent customer satisfaction influences brand loyalty. A questionnaire survey method was used to collect data for this study and 100 footwear consumers from shopping malls located in the Klang Valley, Malaysia participated in this study. This study to determine the influence of these factors on customer satisfaction and brand loyalty. The research findings show that there is positive influence of brand credibility, perceived quality and perceived value on customer satisfaction

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

System identification of a prototype small scale ROV for depth control

This paper present the design and development of a small scale underwater Remotely Operated Vehicle (ROV) and modelling the depth response of this ROV using System Identification Toolbox. The design of a small scale ROV has been done to minimize the hydrodynamic force and increase energy efficiency compared to the previous model that was developed by Underwater Technology Research Group (UTeRG). The performance of the designed ROV will be tested in UTeRG laboratory (lab tank test). The output signal from the pressure sensor (MPX4250GP) and the Inertial Measurement Unit (IMU) sensor are interpreted via an NI-card which was used for the data transfer. The prototype ROV was compared with the previous version in terms of depth control performance. System identification toolbox in MATLAB was used to infer a model from open-loop experiments. Then the obtained model was used to design a controller for the ROV. The focus of the controller design will be to ensure that the ROV is stable and can maintain position at a certain depth in a real underwater environment. After all the experiment has been conducted, the ROV managed to operate in a certain depth underwater using the controller designed successfully

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