Sabariah Julai

Preparation and Characterization of Polyvinyl Alcohol-Chitosan Composite Films Reinforced with Cellulose Nanofiber

In this study microcrystalline cellulose (MCC) was oxidized by 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation. The treated cellulose slurry was mechanically homogenized to form a transparent dispersion which consisted of individual cellulose nanofibers with uniform widths of 3–4 nm. Bio-nanocomposite films were then prepared from a polyvinyl alcohol (PVA)-chitosan (CS) polymeric blend with different TEMPO-oxidized cellulose nanofiber (TOCN) contents (0, 0.5, 1.0 and 1.5 wt %) via the solution casting method. The characterizations of pure PVA/CS and PVA/CS/TOCN films were performed in terms of field emission scanning electron microscopy (FESEM), tensile tests, thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The results from FESEM analysis justified that low loading levels of TOCNs were dispersed uniformly and homogeneously in the PVA-CS blend matrix. The tensile strength and thermal stability of the films were increased with the increased loading levels of TOCNs to a maximum level. The thermal study indicated a slight improvement of the thermal stability upon the reinforcement of TOCNs. As evidenced by the FTIR and XRD, PVA and CS were considered miscible and compatible owing to hydrogen bonding interaction. These analyses also revealed the good dispersion of TOCNs within the PVA/CS polymer matrix. The improved properties due to the reinforcement of TOCNs can be highly beneficial in numerous applications.

Active vibration control of flexible plate structures with distributed disturbances

This paper presents the development of an active vibration control (AVC) system with distributed disturbances using genetic algorithms, particle swarm optimization, and ant colony optimization. The approaches are realized with multiple-input multiple-output and multiple-input single-output control configurations in a flexible plate structure. A simulation environment characterizing a thin, square plate, with all edges clamped, is developed using the finite difference method as a platform for test and verification of the developed control approaches. Simulations are carried out with random disturbance signal. The control design comprises a direct minimization of the error (observed) signal by allowing a collective determination of detection and secondary source locations together with controller parameters. The algorithms are formulated with a fitness function based on the mean square of the observed vibration level. In this manner, knowledge of the input/output characterization of the system is not required for design of the controller. The performance of the system is assessed and analyzed both in the time and frequency domains and it is demonstrated that significant vibration reduction is achieved with the proposed schemes.

Vibration suppression of flexible plate structures using swarm and genetic optimization techniques

This paper presents the development of an active vibration control mechanism using genetic algorithm and particle swarm optimization. The approaches are realized with single-input single-output and single-input multiple-output control configurations in a flexible plate structure with all edges clamped. Simulations are carried out with different disturbance signal types, namely random, pseudo random binary sequence, and finite-duration step. The control design comprises a direct minimization of the error (observed) signal by searching the optimal locations of the detector and secondary source, along with the controller parameters. The algorithms are formulated with an objective function based on mean square of the observed vibration. In this manner, knowledge of the input/output characterization of the system is not required for design of the controller. The performance of the system is assessed and analyzed both in the time and frequency domains and it is demonstrated that the proposed scheme reduces vibration of the flexible plate significantly.

Active vibration control of a flexible plate structure using particle swarm optimization

This paper presents investigations of modeling the flexible plate structures using particle swarm optimization (PSO) and active vibration control (AVC) of such structures. The optimization technique is utilized to obtain a dynamic model of a flexible plate structure based on auto-regressive with exogenous (ARX) input structure. The structure is subjected to two different disturbance signal types, namely random, and finite duration step. The fitness function for the PSO is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed PSO modeling approach is used for AVC system design to suppress the vibration of the flexible plate. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density (PSD) of the observed signal.

SISO and SIMO active vibration control of a flexible plate structure using real-coded genetic algorithm

This paper presents the development of an active vibration control (AVC) mechanism using real-coded genetic algorithm (RCGA) optimization. The approach is realized with single-input single-output (SISO) and single-input multiple-output (SIMO) control configurations in a flexible plate structure. A simulation environment characterizing a thin, square plate, with all edges clamped, is developed using the finite difference (FD) method as a platform for test and verification of the developed control approach. Tests are carried out with different disturbance signal types, namely random and finite duration step. The control design comprises a direct optimization of the controller parameters based on minimization of the error (observed) signal. The RCGA is formulated with a fitness function based on mean square of the observed vibration. The performance of the system is assessed and analysed both in the time and frequency domains and it is demonstrated that the proposed scheme reduces vibration of the flexible plate significantly.

PSO-Based Parametric Modelling of a Thin Plate Structure

Parametric modelling of dynamic structure may benefits from features of particle swarm optimization (PSO), which robust and fast in solving nonlinear, non-differentiable, and multimodal problems. This paper presents the PSO approach which includes an improved algorithm to model a flexible plate structure parametrically. The introduction of a dynamic spread and momentum factor, both by modifying the inertial weight of each particle, solves the problem of getting stuck at local optima, preserves diversity and trades-off between exploration and exploitation. The identification is performed on basis of minimizing the mean-squared output error and is assessed with correlation tests and in time and frequency domains. It is shown input-output characterization in time and frequency domains that the improved algorithm possesses features of accuracy and quick convergence.

Control of a flexible plate structure using particle swarm optimization

An investigation on control mechanism using particle swarm optimization (PSO) to suppress the vibration of flexible plate has been carried out. Active vibration control (AVC) is implemented for the case of single-input single output (SISO), and the controller is realized in linear parametric form where all parameters are arbitrarily chosen by applying the working mechanism of PSO. The objective function is the mean-squared error of the observed vibration signal. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density (PSD) of the observed signal.

Parametric modelling of flexible plate structures using continuous ant colony optimization

This paper presents parametric modelling of flexible plate structures using ant colony optimization (ACO). The global optimization technique of ACO is utilized to obtain a dynamic model of a flexible plate structure based on one-step-ahead prediction. In this paper a proposed ACO with roulette wheel selection known as ACO2 is compared with a previous modified ACO which is denoted as ACO1. The comparison is to recognize the optimum performance and to enhance the fast convergence. The flexible plate structure is subjected to random disturbance signal types. Fitness function for the ACO optimization is the mean-squared error between the measured and estimated output of the plate. The validation of the algorithm is presented in both time and frequency domains. Simulation results show that the proposed approachis better and has a fast convergence rate than ACO1. The developed ACO modelling approach will be used for active vibration control systems design and development in future work.

Parametric modelling of flexible plate structures using real-coded genetic algorithms

This paper presents parametric modelling of flexible plate structures using real-coded genetic algorithms (RCGA). The global optimization technique of RCGA is utilized to obtain a dynamic model of a flexible plate structure based on one-step-ahead (OSA) prediction. The structure is subjected to three different disturbance signal types, namely random, pseudo random binary sequence (PRBS), and finite duration step. The fitness function for the RCGA optimization is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed RCGA modelling approach will be used for active vibration control systems design and development in future work.

Active Vibration Control of a Flexible Plate Structure Using Ant System Algorithm

This paper presents investigations into modeling and active vibration control (AVC) of a flexible plate structure using continuous ant system algorithm (CASA) such structures. The optimization technique is utilized to obtain a dynamic model of a flexible plate structure based on auto-regressive with exogenous (ARX) input model structure. The flexible plate structure is subjected to two different disturbance signal types, namely random, and finite duration step. The fitness function for the CASA is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed CASA modeling approach is used for AVC system design to suppress the vibration of the flexible plate. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density of the observed signal.