Azuwir Mohd Nor
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Preferred name
Azuwir Mohd Nor
Official Name
Azuwir, Mohd Nor
Alternative Name
Mohd Nor, Azuwir
Nor, A. M.
Nor, Azuwir Mohd
Main Affiliation
Scopus Author ID
57201741934
Researcher ID
GOI-2498-2022

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Dynamic modelling and adaptive PID control of palm oil biodiesel engine

2013 , Azuwir Mohd Nor

The use of biodiesel seems set to become a popular alternative fuel for transportation to replace the high price petroleum fuel. To successfully implement the usage of biodiesel in transportation requires good understanding of the engine dynamics and reliable controller to manage the engine. Hence, this study is aimed at the development of mathematical models and adaptive controller of automotive engine fuelled with palm oil methyl esters (palm oil biodiesel). The process modelling investigation started with linear discrete-time single-input-single-output (SISO) dynamic mathematical models representing the relationship between engine speed and engine throttle of a diesel engine test-unit. Both deterministic and stochastic model types are derived and validated. Three parameter estimation techniques of Recursive Least Squares (RLS), Recursive Extended Least Squares (RELS) and Differential Evolution (DE) are used to estimate the engine parameters. Then, the nonlinear dynamic model of the engine type is derived and validated. Orthogonal Least Squares (OLS) estimation technique together with Error Reduction Ratio (ERR) procedures are used in the selection of the parsimonious model structure and parameter estimation for nonlinear ARX (NARX) model. The accuracy of linear and nonlinear dynamic models are compared and analyzed. The results show that all models derived are stable and good in predicting the engine output. Next, adaptive PID speed controller based on pole assignment method was designed, developed, tested and simulated before implemented in real-time on the engine test-unit. The adaptive controller is designed to track and regulate set-point speed as well as reject the disturbance introduced to the system. Throughout the investigation the control algorithm developed is tested at various engine set-point speeds and load disturbances. The results show that the algorithms produce very good dynamic output responses of the palm oil biodiesel engine. The algorithms have successfully achieved the control objective of tracking and regulating the engine speed. Furthermore, the experimental results also proved the disturbance rejection capability of the controller. The performance of the adaptive controller is compared with tracking, regulating and rejecting disturbance of automotive engine fuelled with petroleum diesel. In both cases, the controllers performed very well and proved to be reliable for both types of fuel. This study has significantly proved that adaptive PID speed controller developed performed effectively in controlling automotive engine speed fuelled with palm oil biodiesel and petroleum diesel without engine modification.

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Prediction of the material consumption of PLA plus fused deposition models using artificial neural network technique

2024-04-22 , Nasuha H. , Mohd Sazli Saad , Mohamad Ezral Baharudin , Azuwir Mohd Nor , Mohd Zakimi Zakaria

Fused Deposition Modelling (FDM) is a complex additive manufacturing (AM) process involving multiple process parameters incapable of being modelled with conventional methods such as regression and mathematical modelling. The goal of the study is to develop an Artificial Neural Network (ANN) model that can accurately predict the material consumption of FDM printed parts considering the effect of process parameters such as layer height, infill density, printing temperature, and printing speed to create an ideal model that can optimize the use of resources and reduce material. The experiment was designed using face centered central composite design (FCCCD) yielding 78 specimens that were weighed using a densimeter to identify material consumption. Then, three networks with a different number of hidden layers and neurons were trained to identify the best-performing ANN structure with the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and highest coefficient of determination (R2). The fittest models were modelled and compared to identify the best-performing structure. Results indicated that the ANN model with double hidden layers with 19 and 14 neurons each showed the most precise prediction in modelling material consumption with the lowest MSE of 0.00096.

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Modeling coupled electric drives systems using a modified narmax model

2021-05-03 , Mohd Zakimi Zakaria , Mansor Z. , Azuwir Mohd Nor , Mohamad Ezral Baharudin , Mohd Sazli Saad

The nonlinear auto-regressive moving average with exogeneous input (NARMAX) model known as one of superior type of models to represent a wide class of dynamic systems. In this paper, a modified NARMAX is proposed in modeling dynamic system. The aim is to investigate the performance of the modified NARMAX model and compared to the conventional NARMAX model for modeling CE8 coupled electric drives system. Multi-objective optimization differential evolution (MOODE) algorithm is used as a model structure selection algorithm to obtain the final model from both approached models. Model predicted output (MPO) test is applied in order to reveal the performance of each model. Through the MPO test, it is concluded that the modified NARMAX model offers a better predicted output than conventional NARMAX model.

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Failure envelope modelling of glass/epoxy composite pipes using system identification method

2017-11-07 , Ang Jia Yi , Mohd Shukry Abdul Majid , Azuwir Mohd Nor , Sazali Yaacob , Mohd Ridzuan Mohd Jamir

The paper aims to model the performance of the Glass Fibre Reinforced Epoxy (GRE) composite pipe under multiaxial loading via system identification approach. System identification modelling depends on the input and output data of the experimental result. In this study, the experimental data used are obtained from a pressurised test rig. The model is based on pure hydrostatic (2H: 1A) loading using GRE pipes with three different winding angles (±45°, ±55°, ±63°). Several models based on different model structures are derived for comparison to obtain the best modelling accuracy. The result shows that the transfer function method could model and has the highest efficiency compared with the experimental result. The ±45°pipe model have achieved 92.41% and 85.13% for both its hoop and axial model. The ±55°pipe model has achieved 96.64% and 86.1%. Follow by the ±63°which the best fit is 92.41% and 94.26%. At the last part of this research, the ±55°pipe model and experimental data has been use to identified when the damage occur and found that the axial strain of 78 bar can damage the experimental pipe in this research.

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A comparative study of numerical modelling and analysis for large articulated pendulums

2025-05 , Siti Fatimah Azzahra Ahmad Noh , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Mohd Sazli Saad , Azuwir Mohd Nor

In this article, we present a large system of multiple pendulums, also articulated pendulums, with twenty pendulums as a multibody model. The main objective of the study is to compare the computational time efficiency of two multibody formulations: the augmented Lagrangian and the recursive method for each articulated system. The equations of motion were derived for each formulation and the fourth- and fifth-order Runge-Kutta methods were utilised to solve for the equations by representing the kinematics and dynamics of the systems numerically. The computational times that corresponded to the manipulated step size and tolerance were compared for both formulations. The results showed that the augmented Lagrangian formulation had a significant divergence towards the negative y-axis at tolerance 0.1s for all modified step sizes. The animations also demonstrated elongation for specific pendulums based on the step size selection at a tolerance 0.1s. The recursive method, on the other hand, produced the best-fit plots and stable results for all xy-position and velocity-time plots for each adjusted step size and tolerance. Therefore, the recursive method is concluded to be more efficient than the augmented Lagrangian formulation in solving large open-loop multibody systems.

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System identification of flexible structure using firefly algorithm

2021-05-03 , Mohd Sazli Saad , Noor Fadhilah Mat Ros , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria , Azuwir Mohd Nor

This paper originally concentrates on application of firefly algorithm on modeling of flexible beam. An attempt of obtaining a linear parametric model is accomplished by acquiring the input-output data from the vibration of beam and followed by the selection the auto regressive with exogenous inputs (ARX) model structure as a benchmark for mathematical model of beam. The parameters of the model structure are identified using firefly algorithm. A few sets of parameter settings are tested to find the most optimal value. The best parameter setting estimation is selected based on performance of model. The model is compared with the model from conventional estimation approach and validated using validity test. It is found that firefly algorithm produces the best ARX model with the lowest MSE compared to the least square algorithm.

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Modelling and evolutionary computation optimization on FDM process for flexural strength using integrated approach RSM and PSO

2021-02-01 , Mohd Sazli Saad , Azuwir Mohd Nor , Mohd Zakimi Zakaria , Mohamad Ezral Baharudin , Wan Sallha Yusoff

Fused deposition modelling (FDM) is a modern rapid prototyping (RP) technique due to its potential to replicate a concept modelling, prototypes tooling and usable parts of complex structures within a short period of time. However, proper parameter selection is crucial to produce good quality products with reasonable mechanical properties, such as mechanical strength. In this study, four important process parameters, such as layer thickness, printing speed, print temperature and outer shell speed, are considered. These parameters are studied to observe their relationship towards the flexural strength of the polylactic acid (PLA) printed parts. The experimental design is conducted based on the central composite design in response surface methodology (RSM). Statistical analysis is performed using analysis of variance (ANOVA), in which the correlation between input parameters and output response is analysed. Next, the evolutionary algorithm optimisation approach, i.e., particle swarm optimisation (PSO), is applied to optimise the process parameters based on the regression model generated from the ANOVA. Results obtained from the PSO method are experimentally validated and compared with those of the traditional method (i.e., RSM). The flexural strength from experimental validation obtained using PSO exhibits an improvement of approximately 3.8%. The optimum parameters for layer thickness (A), print speed (B), print temperature (C) and outer shell speed (D) of approximately 0.38 mm, 46.58 mm/s, 185.45 °C and 29.59 mm/s result in flexural strength of 96.62 MPa.

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FDM Parameters Optimization for Improving Tensile Strength using Response Surface Methodology and Particle Swarm Optimization

2024-09-01 , Mohamad Ezral Baharudin , Mohd Sazli Saad , Mohd Zakimi Zakaria , Azuwir Mohd Nor , Ab Talib M.H.

Fused deposition modelling (FDM) is a popular 3D printing technique that uses a thermoplastic filament as the build material. In FDM 3D printing, tensile strength can be an issue because the layers of the object are built on top of each other, and if the layers do not adhere properly, the object can be weak and prone to breaking. Typically, this problem is caused by incorrect parameter settings. Hence, this study was then carried out to analyse and improve the printing quality in term of tensile strength of the printed part using the response surface methodology (RSM) and the particle swarm optimization (PSO) method. The effect of four input parameters such as layer height, printing speed, infill density, and print temperature was examined on the tensile strength of polylactic acid (PLA) standard samples ASTM D638-IV. The experimental design was performed using face-centred central composite designs (FCCD). The experimental data were statistically analysed to form a regression model of the tensile strength. This model was used to approximate the actual process. The optimization was performed using desirability analysis from RSM and PSO to search for the optimal parameter for maximum tensile strength. Experimental results showed that PSO outperformed RSM with a 1.52 % reduction in tensile strength. The maximum tensile strength obtained from PSO was about 39.069 MPa with the optimal process parameters of layer height of 0.30 mm, printing speed of 30.17 m/s, infill density of 79.72 %, and print temperature of 205.92 °C.

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ARx modeling of flexible beam system using bat algorithm

2021-05-03 , Mohd Sazli Saad , Noor Fadhilah Mat Ros , Mohd Zakimi Zakaria , Mohamad Ezral Baharudin , Azuwir Mohd Nor

This paper describes the development of dynamic model of flexible beam system using system identification method based on nature inspired algorithm i.e. bat algorithm. At first, input-output data from the experimental rig of flexible beam were collected such that input signal is taken from piezo actuator and output signal from the laser displacement sensor. Then, linear parametric model structure is accomplished using auto regressive with exogenous inputs (ARX). The optimal parameters of the ARX model are identified using bat algorithm. The best parameter setting estimation is selected based on the best fit criterion i.e mean square error (MSE). The identified model is compared with the model from conventional estimation approach. Simulation results show that bat algorithm can outperform the least square algorithm in parametric modelling of the flexible beam.

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Addressing labour ergonomics through automation in oil palm plantation activities a necessity for sustainable agriculture

2024 , Azuwir Mohd Nor , Mohd Sazli Saad , Mohamad Ezral Baharudin , Mohd Zakimi Zakaria

A key element of the potential of robotics is understanding how effective automation can improve labour‐intensive jobs while also considering worker ergonomics. These sectors often depend on manual labour, which exposes employees to considerable ergonomic stress, especially musculoskeletal disorders (MSDs) that can result from repetitive and physically demanding activities like harvesting, pruning, and lifting heavy items. By coordinating automation tools such as harvesters, unloaders, and driverless carts with the various manual tasks that workers perform, we can significantly lower safety risks. The main objective of introducing automation is to reduce the physical strain on workers, which not only aims to alleviate MSD‐related health problems but also helps to lessen worker fatigue. Effectively integrating artificial intelligence (AI) and big data analytics will improve workforce efficiency, making the Brightfield industry stronger. Transitioning from manual tasks to automated solutions is just the initial step toward enhancing production in this field. By tackling these ergonomic issues through automation, this paper highlights the dual advantages of promoting worker health and increasing productivity in the industry.

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