Tuning Factor the Single Input Fuzzy Logic Controller to Improve the Performances of Depth Control for Underwater Remotely Operated Vehicle

This paper investigates the factor of tuning variable parameter for Single Input Fuzzy Logic Controller (SIFLC) to improve the performances of depth control for the underwater Remotely Operated Vehicle (ROV). This study and investigates will focus on the number of rules in SIFLC, lookup table, slope of a linear equation, and also model reference to give optimum performances of depth control without overshoot in system response and faster rise time and settling time. The variable parameter for SIFLC is tuned by Particle Swarm Optimization (PSO) algorithm. The investigation focused on the number of rules will be reduced, SIFLC parameter reduced, represented lookup table as a linear control surface method to represent the inference engine of FLC. The investigation on model reference also covered in this paper. The current of model reference will give the best system response for depth control. The slope of linear equation either in positive and negative values and come up from conventional FLC then will simplify into SIFLC. The results obtained the number of rules, the slope of linear equations and model reference will be affecting the results of system performances

Comparison Of Controllers Design Performance For Underwater Remotely Operated Vehicle (ROV) Depth Control

This paper presented controller designs utilized in controlling the ROV depth control system which involved Single Input Fuzzy Logic Controller (SIFLC),Adaptive Neural Fuzzy Inference System (ANFIS),Mamdani Fuzzy Logic Controller (M-FLC) and Proportional Integrated Differential (PID) controller.The model of ROV was generate using MATLAB System Identification Toolbox’s to gain a transfer function representing the ROV model.This ROV design focused on depth control.The main objective of this study was to analyze the performance of system response among the Controller designs.This controller was verified and validated in MATLAB/Simulink platform.The result showed the analysis performances of the system response in terms of rise time and percentage of overshoot

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

An Improved Of Dual Single Input Fuzzy Logic Controller For Underwater Remotely Operated Vehicle (ROV) – Depth Control

This paper presents an improvement of Dual Single Input Fuzzy Logic Controller (DSIFLC) of an underwater Remotely Operated Vehicle (ROV) system for depth control. Proportional Integral Derivative (PID) controller are used as the basic controller and compared with the SIFLC controller.The technique used is the conventional Fuzzy Logic Controller simplified to Single Input Fuzzy Logic Controller (SIFLC) by using signed distance method.The SIFLC were upgraded to DSIFLC by using double feedback of disturbance.The controller was upgraded until the system response shows the satisfied result in terms of rise time and percentage of overshoot.This method was verified and validated in MATLAB/Simulink platform.The result shows it was found the proposed method have better performances analysis of the system response which is faster rise time and lower percentage of overshoot

Design And Development Of Auto Depth Control Of Remotely Operated Vehicle Using Thrusters System

Remotely Operated Vehicles are underwater robots designed specifically for surveillance, monitoring and collecting data for underwater activities. In the underwater vehicle industries, the thruster is an important part in controlling the direction, depth and speed of the ROV. However, there are some ROVs that cannot be maintained at the specified depth for a long time because of disturbance. This paper proposes an auto depth control using a thruster system. A prototype of a thruster with an auto depth control is developed and attached to the previously fabricated UTeM ROV. This paper presents the operation of auto depth control as well as thrusters for submerging and emerging purposes and maintaining the specified depth. The thruster system utilizes a microcontroller as its brain, a piezoresistive strain gauge pressure sensor and a DC brushless motor to run the propeller. Performance analysis of the auto depth control system is conducted to identify the sensitivity of the pressure sensor, and the accuracy and stability of the system. The results show that the thruster system performs well in maintaining a specified depth as well as stabilizing itself when a disturbanceoccurs even with a simple proportional controller used to control the thruster, where the thruster is an important component of the ROV

Optimization of an Intelligent Controller for an Unmanned Underwater Vehicle

 Underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain it position at a certain depth in order to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an unmanned underwater vehicle is described in this paper. The control algorithm is simulated by using the marine guidance navigation and control simulator. The project shows a radial basis function metamodel can be used to tune the scaling factors of a fuzzy logic controller. By using offline optimization approach, a comparison between genetic algorithm and metamodeling has been done to minimize the integral square error between the set point and the measured depth of the underwater vehicle. The results showed that it is possible to obtain a reasonably good error using metamodeling approach in much a shorter time compared to the genetic algorithm approach

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL

Effect Of Double Fuzzy Logic Controller (DFLC) Based On Power System Stabilizer (PSS) On A Tie- Line Two Generators System

This research was proposed a new type of power system stabilizer based on fuzzy set theory, to improve the dynamic performance of a multi-machine power system. To have good damping characteristics over a wide range of operating conditions, speed deviation and it is derivative of a machine are chosen as the input signals to the fuzzy stabilizer on that particular machine. Fuzzy logic controller (FLC) Two area symmetrical systems connected via tie-line are measured to show via performance of these controllers. This research presents the analysis of change of speed (Δω), change of angle position (Δδ) and tie - line power flow (Δp). In tie-line system two generators control arrangement single fuzzy logic controller (SFLC) have been used as a primary controller, whereas double fuzzy logic controller (DFLC) used as a secondary controller. In addition to this, the system shows comparative between two controller single and double fuzzy controller has been used for the system to achieve the best results using Simulink/MATLAB. Double fuzzy controller has a greater effect on the tie-line system and become more smoothing than single fuzzy controller because has increased the damping of the speed Δω, angle rotor Δδ and power Δp