Investigation on the Behaviour of Weapon Positioning System on Volatile Warship

Combat Management System, CMS is very important for military ships in order to monitor and control the equipment where stability is one of the most important requirements. Many researches have been conducted on weapon control module to achieve accurate and precise shot. Electromechanical analog ballistic computer has been used for accurate firing solution that could automatically control fire guns. Considering static ships, the existing controller has high degree of accuracy but the performance may deteriorate for moving ships that are affected by disturbances. This research has partly solved the issues of stability volatile ship for military application. As a step in developing a control system that is stable and robust toward disturbances with high degree of accuracy, a mathematical model of volatile ship has been developed using System Identification. The developed mathematical model has been tested and verified using the data obtained from the small ship prototype. Results showed that the curve fit achieved 70% accuracy. For future improvements, robust control has been proposed to achieve stability of weapon control module. Quantitative Feedback Theory, QFT could be used to design the robust controller for the weapon control module and the movement of the ship is considered as uncertainty. With pre-defined specifications, the designed controller would be robust for the specified uncertainty range. The mathematical model of the weapon module has been designed and developed by Muslim engineers, the novel weapon control module is considered as part of the contributions of the Islamic civilization to humanity

IIUM bus on campus monitoring system

Malaysia has invested on public transport over the years to improve quality of leaving. Transportation has been identified as the key area in Malaysia’s government transformation programme. In Malaysia, many people still choose to use their own transport compared to public transport to travel. For efficient public transport service such as bus, real-time tracking/monitoring must be available apart from their fixed schedule. The objective of this project is to develop a low cost real-time IIUM bus on campus monitoring system. IIUM shuttle bus monitoring has been chosen as a pilot study which reflects the bigger picture of Malaysia’s bus system. In IIUM, students need to wait for the bus without any clue and this is such a waste of time. Thus, IIUM bus tracking system is proposed in this project where waiting time can be improved. Users can track the exact location of the bus therefore they can have better plan for their journey. Global positioning system technology has been used to send and receive the coordinate data to the server. A web server application has been developed to integrate the Global positioning system data to be presented on the map. At the end of the project, a website of real-time IIUM bus on campus monitoring system has been developed where users can monitor the exact location of the IIUM bus conveniently

Design of QFT-based self-tuning deadbeat controller

This paper presents a design method of self-tuning Quantitative Feedback Theory (QFT) by using improved deadbeat control algorithm. QFT is a technique to achieve robust control with pre-defined specifications whereas deadbeat is an algorithm that could bring the output to steady state with minimum step size. Nevertheless, usually there are large peaks in the deadbeat response.By integrating QFT specifications into deadbeat algorithm, the large peaks could be tolerated. On the other hand, emerging QFT with adaptive element will produce a robust controller with wider coverage of uncertainty. By combining QFT-based deadbeat algorithm and adaptive element, superior controller that is called self tuning QFT-based deadbeat controller could be achieved. The output response that is fast, robust and adaptive is expected. Using a grain dryer plant model as a pilot case-study, the performance of the proposed method has been evaluated and analyzed. Grain drying process is very complex with highly nonlinear behaviour, long delay, affected by environmental changes and affected by disturbances. Performance comparisons have been performed between the proposed self-tuning QFT-based deadbeat, standard QFT and standard dead-beat controllers. The efficiency of the self-tuning QFT based dead-beat controller has been proven from the tests results in terms of controller’s parameters are updated online, less percentage of overshoot and settling time especially when there are variations in the plant

Development of eCall for Malaysia's automotive industries

—Road accidents are one of the most common causes of death among Malaysian citizens. The common problem when accident happened is drivers often have inaccurate awareness of their location, especially on interurban roads or abroad. There are also victims who are injured or trapped and they may not be able to call emergency Call Centre. The scenario may be worsen if there are no witnesses or passersby near the victims to assist them. In order to decrease the rate of fatal accident in Malaysia, eCall system is proposed. The main objective of this research is to develop eCall system that can transmits a call to the public service answering point along with certain vehicle related information using microcomputer, GSM and GPS technologies. The eCall system is able to work automatically or manually depends on the victim’s need. As such, collision detection and voice communication algorithm has been designed. For the collision detection, algorithm has been developed based on the data acquired from gyroscope and accelerometer. The location of incident has been identified using GSM cell phone and GPS location capability. The device has a corresponding infrastructure of Public Safety Answering Points (PSAPs) for the voice communication to inform the emergency emergency response unit about the accident, the level of seriousness and the location of the incident. The eCall prototype has successfully designed and developed. Experimental results shown that the prototype is able to detect collision, identify the GPS coordinates of the accident and able to transmit sound wave from speaker to microphone, to be forwarded to the emergency Call Centre

Robust adaptive LQR control of nonlinear system application to 3-Dof flight control system

This paper contains a new proposed regarding on robust adaptive control method merge with Linear-Quadratic Regulator (LQR), to design a faster response controller for uncertain characterized three degree of freedom (3-DOF) flight control module. 3-DOF helicopter is a bench-top module use in laboratory for experimental purposes only. From the previous experiments, it has seen that the transient response of designed PD controller has significantly very large steady state error which is around 50%. For highly uncertain plants it is highly destructive. A 3-DOF flight control system or bench-top helicopter developed by Quanser is intrinsically nonlinear, unstable and totally uncertain because of the nature of three individual angles well-known as pitch, travel and elevation. The target of this proposed control design is to improve the performance of three angles control of 3-DOF helicopter by integration of LQR controller and robust adaptive controller. Usually standard adaptive controller will produce zero steady state error. But for achieving faster response with zero steady state error is quite difficult. Therefore, this paper proposed a robust adaptive with deadbeat algorithm to overcome the limitations. Our proposal is to introduce robustness to parameter uncertainties and disturbances, by using adaptive laws for the plant parameters’ uncertainty, as a replacement for the traditional ones. This controller may handle large parameter uncertainties and disturbance with rugged stability. The arbitrary combined optimizing method is engaged in this design to optimize the overall performance of the controller. Simulation results and equations are used to demonstration the effectiveness of the proposed control methodology

A development of self-tuning quantitative feedback theory

This paper presents a development of self-tuning Quantitative Feedback Theory (QFT) for a non linear system. QFT is one type of robust controller which deals with plant uncertainty. The performance of robust controller for any uncertain plant is guaranteed based on pre-defined specifications. Meanwhile, self-tuning controller is one type of adaptive controller which also meant to solve the same control problem, however for slower plant drift. By combining both adaptive and robust controllers, both robust and adaptive performance can be achieved. The proposed algorithm is tested on a chosen case study, grain dryer plant. Grain dryer is a non linear plant with uncertainty as the characteristics of the plant can be affected by environmental changes, manufacturing tolerance and input/output disturbance. Based on the results obtained from this case study, the superiority of the proposed self-tuning QFT has been proven. From the comparison test conducted between self-tuning and standard QFT-based controllers, the proposed method produced more desirable response in terms of faster settling time, less percentage of overshoot with reduced ringing, smaller control effort required and wider leverage of uncertainty range

Trajectory tracking of quaternion based quadrotor using model predictive control

The aim of this paper is to introduce the trajectory tracking with a quaternion based quadrotor operation using model predictive con-trol (MPC). Since the efficacy of MPC on a system under noise and disturbance has been distinguished, it is a fair and successful attempt to apply MPC on the quaternion based quadrotor, which is a quite well-known system with uncertainties during operation. Quaternion approaches singularity-free orientation that is advantageous to design any trajectory for quadrotor wherein roll or pitch angle reaches at 90o. As a quaternion, with its four-tuple characteristics that incorporate vector elements, is different from Euler-angle orientation, a new cost function has been developed for the respective MPC controller. In order to achieve singularity-free ori-entations and abate the model infidelity of the system, the quaternion and MPC algorithm have been incorporated for quadrotor flight. Simulation based results elucidate the success of trajectory tracking of quaternion based dynamics of quadrotor using MPC approach

Wireless power transmission system based on magnetic inductive resonance of couple circuit

In this paper, a couple circuits are used to transfer power by magnetic inductive resonance or wireless inductive power transfer method where the individual nodes are batteryless to make them maintenance free. Magnetic inductive resonance mainly works on Electromagnetic field induced between two coils that are tuned to resonate at the same frequency. This type of method has a high Q(quality Factor) and Consist of air cored to avoid 'iron' losses. Wireless or batteryless sensors can be used for energy harvesting and energies are stored in capacitors. Microwave power transmission is a wireless power transmission scheme that can interfere with data transmission where magnetic inductive resonance suitable for data transmission and acquisition

Neuro-fuzzy modeling of a conveyor-belt grain dryer

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model

Evaluation of the effect of walking speeds on human gait recognition

Human Gait Recognition is typically alluded to imply the human ID by the style/way individuals stroll in picture sequences. Our point is to execute the customary gait recognition calculation and to demonstrate the variety in recognition when subject is watched parallel to camera under three conditions- walking slow, at typical speed and walking quickly. For this situation, the work devises a novel strategy with the end goal of likeness calculation as opposed to the customary idea, where the overall recognition rate of 65% percent was achieved