Hanim Mohd Yatim

Particle swarm optimization for identification of a flexible manipulator system

This paper presents an investigation of system identification using parametric modeling approaches for a single-link flexible manipulator system. The utilization of a particle swarm optimization (PSO) technique for modeling of a highly non-linear system is studied in comparison to the conventional recursive least squares (RLS) technique. A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) approach to acquire the input-output data of the system. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. A comparative assessment of the two models in characterizing the manipulator system is presented in time and frequency domains. Results demonstrate the advantages of PSO over RLS in parametric modeling. The developed model achieved will be used for control design and development in future work.

Parametric identification and dynamic characterisation of flexible manipulator system

This paper presents an investigation of an intelligence modeling technique for dynamic characterization of a single-link flexible manipulator system. The flexible manipulator system was first modeled using finite difference (FD) method. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. Performance of the algorithm is compared with the manipulator theoretical natural frequencies obtained analytically. Next, a parametric identification of the system is developed using the conventional Least Square (LS) algorithm and the intelligent Genetic Algorithm (GA). Comparative assessment is presented for validation of the model in characterizing the manipulator system in frequency domain. The developed genetic-modeling approach will be used for control design and development in future work.

Active vibration control of flexible plate with free-free-clamped-clamped edges using genetic algorithm

This paper presents an investigation of system modeling using genetic algorithm and active vibration control of flexible plate structure. The experimental rig was designed and fabricated with free-free-clamped-clamped edges boundary condition in this research. The experimental study was conducted using experimental rig complete with data acquisition and instrumentation system to collect the input-output data of flexible plate structure. This input-output data used to develop the system identification to obtain a dynamic model of flexible plate based on auto-regressive with exogenous input structure. The developed model using genetic algorithm were validated using mean squared error, one step-ahead prediction and correlation test. The fitness function of genetic algorithm is mean squared error between the measured and estimated outputs of flexible plate. The validations of developed model were presented in time domain and frequency domain. The modeling of flexible plate using genetic algorithm was used in active vibration control system design for vibration suppression on the plate structure. The performance of developed controller assessed in term of spectral attenuation obtained for resonance modes.

Modelling and control of horizontal flexible plate using particle swarm optimization

This paper presents the modeling and active vibration control using an evolutionary swarm algorithm known as particle swarm optimization. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges as boundary conditions constrained at horizontal position. The purpose of the experimental rig development is to collect the input-output vibration data. Next, the data acquisition and instrumentation system were designed and integrated with the experimental rig. Several procedures were conducted to acquire the input-output vibration data. The collected vibration data were then utilized to develop the system model. The parametric modeling using particle swarm optimization was devised using an auto regressive model with exogenous model structure. The developed model was validated using mean square error, one step ahead prediction, correlation tests and pole-zero diagram stability. Then, the developed model was used for the development of controller using an active vibration control technique. It was found that particle swarm optimization based on the active vibration control using Ziegler-Nichols method has successfully suppressed the unwanted vibration of the horizontal flexible plate system. The developed controller achieved the highest attenuation value at the first mode of vibration which is the dominant mode in the system with 34.37 dB attenuation.

Development of controller graphical user interface for vibration suppression of flexible beam

This study proposes the implementation of Active Vibration Control (AVC) via a well-designed Graphical User Interface (GUI) to suppress the unwanted vibration of flexible beam system. The flexible beam system is the model to be controlled which is obtained from System Identification technique. A Proportional-Integral-Derivative (PID) controller is implemented in this study with three types of tuning methods: heuristic tuning, auto-tuning, and Particle Swarm Optimization tuning. A GUI controller is designed and developed using MATLAB platform. The GUI controller is a user friendly application that can be implemented for controller design and learning. The results are displayed in graphical plots and numerical values. In this study, the GUI controller is well-designed and implemented successfully for controller design. In addition, it was found that PI controller with auto tuning has the superiority in vibration suppression of the flexible beam system.

Modeling of flexible manipulator structure using particle swarm optimization with Explorer

This paper presents an investigation into the development of novel Particle Swarm Optimization with Explorer and its application to system identification for a single-link flexible manipulator system. A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) method to acquire the input-output data of the system. In this study, system identification scheme is developed to obtain a dynamic model of the manipulator in parametric form using Particle Swarm Optimization with Explorer. The introduction of explorer solves problem of getting stuck at local minima, thus proposed a novel methodology namely as Particle Swarm Optimization with Explorer (PSOE). Its performance is assessed in comparison to a standard Particle Swarm Optimization in characterizing the flexible manipulator structure. Results demonstrate the advantages of Particle Swarm Optimization with Explorer over their standard counterpart in system identification.

Modeling flexible plate structure system with free-free-clamped-clamped (FFCC) edges using Particle Swarm Optimization

This paper presents the performance of system identification for rectangular flexible plate using conventional parametric modeling approach by Recursive Least Squares (RLS) and intelligent parametric modeling approach by Particle swarm Optimization (PSO). The experimental rig of rectangular flexible plate with free-free-clamped-clamped edges boundary condition designed and fabricated in this research. The experimental study was conducted to collect the input-output data using a vibration flexible plate complete with data acquisition and instrumentation system. The input-output data will be used during developed the system identification. The whole developed model using RLS and PSO were validated using mean squares error (MSE), one step ahead prediction (OSA) and correlation test. The estimated of developed models was found are comparable, acceptable and possible to be used as a platform of controller development later on. As a comparison between developed system, it was found that the Particle Swarm Optimization algorithm has perform better in term of the lowest mean squares error which is 0.00032719.