Shahrum Shah Abdullah

Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle

This paper describes a control scheme that provides an efficient way to design a Fuzzy Logic Controller (FLC) for the unmanned underwater vehicle (UUV). The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), reduces the conventional two-input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC offers significant reduction in rule inferences and simplify the tuning of control parameters. Practically it can be easily implemented by a look-up table using a low cost microprocessor due its piecewise linear control surface. To verify its effectiveness, the control algorithm is simulated using the Marine Systems Simulator (MSS) on the Matlab/Simulink® platform. The result indicates that both the SIFLC and CFLC give identical response to the same input sets. However SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.

Thruster Modelling for Underwater Vehicle Using System Identification Method

Abstract This paper describes a study of thruster modelling for a remotely operated underwater vehicle (ROV) by system identification using Microbox 2000/2000C. Microbox 2000/2000C is an XPC target machine device to interface between an ROV thruster with the MATLAB 2009 software. In this project, a model of the thruster will be developed first so that the system identification toolbox in MATLAB can be used. This project also presents a comparison of mathematical and empirical modelling. The experiments were carried out by using a mini compressor as a dummy depth pressure applied to a pressure sensor. The thruster model will thrust and submerge until it reaches a set point and maintain the set point depth. The depth was based on pressure sensor measurement. A conventional proportional controller was used in this project and the results gathered justified its selection.

Gold price prediction using radial basis function neural network

Gold is a precious metal once widely used as a standard for monetary exchange but was replaced by paper currency mostly used today. However interest in gold trading and investment has resurfaced recently in Malaysia probably due to its price stability. Samples of gold that are used as investment include the Kijang Emas, Public Dinar and the Public Gold which are currently available to the general public in Malaysia. This project will involve developing a system to aid a gold investor in deciding the best time in the future to buy or sell gold. The system developed is based on existing gold data time series and algorithms based on Artificial Neural Networks. The system should be able to give daily prediction to its users.

Modeling and control of magnetic levitation system via fuzzy logic controller

Fuzzy logic controller (FLC) is an attractive alternative to existing classical or modern controllers for designing the challenging Non-linear control systems. It does not require any system modeling or complex mathematical equations governing the relationship between inputs and outputs. Fuzzy rules are very easy to learn and use, even by non-experts. It typically takes only a few rules to describe systems that may require several lines of conventional software code, which reduces the design complexity. By considering these advantages, this paper presents the design and analysis of a FLC controller for the magnetic levitation system. Additionally, a classical PID controller is also designed to compare the performance of both types of controllers. Results reveal that FLC found to give better transient and steady state results compare to the classical PID.

An alternative approach to design a Fuzzy Logic Controller for an autonomous underwater vehicle

This paper presents a control scheme that provides an efficient and a simple way to design a Fuzzy Logic Controller (FLC) for the autonomous underwater vehicle (AUV). The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), condenses the conventional two-input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC significantly reduces the rules and simplifies the tuning of control parameters. Practically, it can be easily implemented by a look-up table using a low cost microprocessor due to its piecewise linear control surface. To verify the effectiveness of the designed controller, the control algorithm is simulated using the Marine Systems Simulator (MSS) on the Matlab/Simulink® platform. The result clearly indicates that both the SIFLC and CFLC give almost identical response to the same input sets. However SIFLC requires very minimum tuning effort and its execution time is in the orders of two magnitudes less than CFLC.

Development of fuzzy logic water bath temperature controller using MATLAB

In this paper, the development of a water bath temperature system to control the liquids temperature in the water bath will be presented. In order to develop the water bath temperature control system, MATLAB fuzzy logic toolbox will be utilized. The Fuzzy Logic Controller (FLC) is designed to control water temperature based on the input acquired from the thermal transducer sensor. The inference engines that are used are; Max-min (Mamdani) method and centre of gravity technique for defuzzification. The 4 × 4 matrix rules for the controller will be used in this project. In addition, the USB NI-DAQ card will be used as xPC target to link the MATLAB software and real time application. The real time output temperature is validated based on the simulations temperature which is initially set by user. It can be concluded the performance of real time study is in par with the simulation result.

Optimization of depth control for Unmanned Underwater Vehicle using surrogate modeling technique

The underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain its position at a certain depth in order for it 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 is to show how a Radial Basis Function Surrogate Model can be used to tune the scaling factors of fuzzy logic controller quickly. By using offline optimization approach, Surrogate Modeling or sometimes called Metamodeling has been done to minimize the Integral Square Error between the set point and the measured depth of the Unmanned Underwater Vehicle.

Study of the effect in the output membership function when tuning a Fuzzy Logic Controller

This paper describes a study of tuning process for fuzzy logic controller (FLC) design. In fuzzy logic controller design, there is no systematic procedure to tune fuzzy logic controller to follow a desired set point. The tuning process of Fuzzy Logic Controllers (FLCs) using trial-and-error approach is commonly done until satisfactory results are obtained. This is usually a tedious and time-consuming task but it has been widely employed and has been used in many successful industrial applications. The performance of the system can be analyzed. If the results are not as desired, changes are made either to the number of the fuzzy partitions or the mapping of the membership function and then the system can be tested again. This paper demonstrates a faster tuning process by adjusting the mapping of the membership function to get desired output. Through identifying and analyzing what will be done on adjusting mapping of membership functions by utilizing knowledge from the experts, it will be demonstrated in this paper that the tuning process of FLCs can be easily simplified.

Contribution of NaP-Channels to the Property of Subthreshold Resonance Oscillation

Subthreshold resonance oscillations are observed in many excitatory/inhibitory neurons in our brain. Although they have been thought to play an important role in behavioral or perceptual states in animals, detail properties of these phenomena have not been clarified, yet. It is necessary to understand first these oscillatory features to clarify the contribution of these rhythmic oscillations to higher brain function, such as short-term memory, the working memory, long-term potentiation and long-term depression. Among various voltage-dependent channels thought to be involved in the generation of these oscillations, hyperpolarization-activated potassium channel (h channel) and persistent inactivating sodium channel (NaP) are considered, because these two voltage-dependent channels are closely related to the sustained oscillatory activity observed in Entorhinal cortex and other Neocortex regions. This feature article considers a compartmental neuron model with an h-channel and a NaP-channel. The NaP-channel contribution to the property of subthreshold resonance oscillation is examined by computer simulation of this neuron model.

PID plus LQR attitude control for hexarotor MAV in indoor environments

The aim of this paper is to propose an optimum linear control algorithm that is able to stabilize the attitude of a hexarotor micro aerial vehicle (MAV) in indoor environment. The work will then compare it with the classical control PID (proportional-integral-derivative) and optimal control LQR (linear quadratic regulator) techniques. The proposed attitude controller is based on an outer-inner loop structure PID plus LQR (PID+LQR) control method where the controller combines the positive features of each sub-controller (PID and LQR). The key contribution of this work is the proposed attitude controller can improve robustness and transient response thus leads to faster response in indoor flying environment. The attitude tracking errors are proven to be ultimately bounded with specified boundaries. Simulation results on the hexarotor demonstrate the effectives of the proposed attitude controller.