Azuwir Mohd Nor, Dr.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/436872019-04-24T04:37:09Z2019-04-24T04:37:09ZMicrocontroller-based for system identification tools using least square method for RC circuitsJia Yi, AngAbdul Majid, M.S.,Azuwir, Mohd NorYaacob, S.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/443082016-12-02T08:13:22Z2015-01-01T00:00:00ZMicrocontroller-based for system identification tools using least square method for RC circuits
Jia Yi, Ang; Abdul Majid, M.S.,; Azuwir, Mohd Nor; Yaacob, S.
System identification is one of the method to construct a plant mathematical model from experimental data. This method has been widely applied in the automatic control, aviation, spaceflight medicine, society economics and other fields more. With the rapid growth of the science and technology, the system identification technique has increasingly grown in various applications. Since most of the system identification devices are off-line base, this means that the system identification can only be done after collecting the data and process through a computer devices. This paper will show how to process system identification method with real-time system. This method required a microcontroller as the medium to perform. Thatâ€™s why the system identification method will be programmed into a microcontroller, based on Least Square Method. Later, the system will be tested on a RC circuit to see the effect of the signal and the mathematical model obtained. The data will undergo the system identification toolbox for process using ARX and ARMAX model. On the other hand, the data will also be collected using the microcontroller created for analysis purpose. To ensure the validity of the model some verification methods are performed. Results show that the Least Square Method using Microcontroller base has the capability to work as a system identification tools.
Link to publisher's homepage at http://www.jurnalteknologi.utm.my
2015-01-01T00:00:00ZCrashworthiness study of S-Rail behaviorMohd Sabri, HussinHadi, Hasnul A.Siti Aishah, AdamAzuwir, Mohd NorZaiazmin, Y. N.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/437562016-10-21T07:38:16Z2011-11-01T00:00:00ZCrashworthiness study of S-Rail behavior
Mohd Sabri, Hussin; Hadi, Hasnul A.; Siti Aishah, Adam; Azuwir, Mohd Nor; Zaiazmin, Y. N.
In this study, a different design aspect of a simplified front side rail structure of an automobile body (S-Rail) from the point of view of crashworthiness parameters which are crushed energy absorption and force response and also weight efficiency is studied. Various orientations of cross section design and various material replacements have been applied to investigate their effects. The specific energy absorption (i.e. Energy absorption per unit weight) is taken as a measure of the performance of a structure. Effect of different cross section with model cross section horizontally 'hat - type' model (shape 5) given greatest energy absorbed. This model then being analyzed with different material such as mild steel, aluminum, Hastelloy X alloy and Fiberglass Polyamide (PA - 66). Hastelloy X alloy result the highest increment in ability to absorbed energy during collision. The consideration of various cross sections and the best material selection of replacement then been discussed and compared for suitability from the aspect of crashworthiness, safety of passengers, weight efficiency and cost to automobile industry.
Link to publisher's homepage at http://www.praiseworthyprize.org
2011-11-01T00:00:00ZModeling and validation of brushless DC motorMohd Sabri, HussinAzuwir, Mohd NorNor Zaiazmin, Yahayahttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/148772011-10-24T04:08:30Z2011-04-19T00:00:00ZModeling and validation of brushless DC motor
Mohd Sabri, Hussin; Azuwir, Mohd Nor; Nor Zaiazmin, Yahaya
A black box modeling for a Brushless Direct Current motor is developed and simulated based on real-time data. Taking a discrete time form for the system model, an ARX model structure was selected in this work. The real-time data acquired using a Contact Multimeter Probes via NI data acquisition system from a Brushless Direct Current motor. A Pseudo-Random Binary Sequence has been used as the input signal to determine the open-loop model of brushless motor at determined speeds. Input signal and measured data were interfaced to the plant via Matlab programming. Matlab toolbox was used to obtain the estimates model. The model validation was produced by model output plot which is comparing the input output and gives the percent best fit.
Link to publisher's homepage at http://ieeexplore.ieee.org/
2011-04-19T00:00:00ZIdentification of dynamic linear models of automotive palm bio-diesel engine for real-time speed control applicationsAzuwir, Mohd NorMohd Zaki, Ab. Muin, Prof. Dr.Abdul Hamid, Adom, Prof. Madya Dr.http://dspace.unimap.edu.my:80/xmlui/handle/123456789/148132011-10-23T13:45:22Z2011-04-19T00:00:00ZIdentification of dynamic linear models of automotive palm bio-diesel engine for real-time speed control applications
Azuwir, Mohd Nor; Mohd Zaki, Ab. Muin, Prof. Dr.; Abdul Hamid, Adom, Prof. Madya Dr.
This paper describes the black-box modeling of automotive palm bio-diesel engine based on real-time data. The models derived are for the development of real-time self tuning speed controller purposes. Assuming a discrete time form for the system model, an Autoregressive eXogenous (ARX) model structure was selected in this work. Real-time data obtained using a computer-based data acquisition system from a 2.0L automotive diesel engine test-bed unit were used for modeling. A Pseudo-Random Binary Sequence (PRBS) with maximum lengths sequence of 31 has been used as the input signal to determine the dynamic model of automotive palm diesel engine at low, medium and high speed range (around 1300, 2150 and 3250 rpm). The input and output signals were interfaced to the plant via Matlab programming. A recursive estimation algorithm, Recursive Least Squares (RLS) method, was used to estimate of the parameters of the models. Finally, model validation test was done by plotting the output predicted by the model and comparing it with the measured output.
Link to publisher's homepage at http://ieeexplore.ieee.org/
2011-04-19T00:00:00Z