Intan Zaurah Mat Darus

Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system

This paper focuses on an identification technique based on genetic algorithms (GAs) with application to rectangular flexible plate systems for active vibration control. A real coded GA with a new truncation-based selection strategy of individuals is developed, to allow fast convergence to the global optimum. A simulation environment characterizing the dynamic behavior of a flexible rectangular plate system is developed using the central finite difference (FD) techniques. The plate thus developed is excited by a uniformly distributed random disturbance and the input-output data of the system acquired is used for black-box modeling the system with the GA optimization using an autoregressive model structure. Model validity tests based on statistical measures and output prediction are carried out. The prediction capability of the model is further examined with unseen data. It is demonstrated that the GA gives faster convergence to an optimum solution and the model obtained characterizes the dynamic system behavior of the system well.

Self-learning active vibration control of a flexible plate structure with piezoelectric actuator

Abstract In this paper, an active vibration control (AVC) incorporating active piezoelectric actuator and self-learning control for a flexible plate structure is presented. The flexible plate system is first modelled and simulated via a finite difference (FD) method. Then, the validity of the obtained model is investigated by comparing the plate natural frequencies predicted by the model with the reported values obtained from literature. After validating the model, a proportional or P-type iterative learning (IL) algorithm combined with a feedback controller is applied to the plate dynamics via the FD simulation platform. The algorithms were then coded in MATLAB to evaluate the performance of the control system. An optimized value of the learning parameter and an appropriate stopping criterion for the IL algorithm were also proposed. Different types of disturbances were employed to excite the plate system at different excitation points and the controller ability to attenuate the vibration of observation point was investigated. The simulation results clearly demonstrate an effective vibration suppression capability that can be achieved using piezoelectric actuator with the incorporated self-learning feedback controller.

Non-parametric modelling of a rectangular flexible plate structure

This research investigates the performance of dynamic modelling using non-parametric techniques for identification of a flexible structure system for development of active vibration control. In this paper, the implementation details are described and the experimental studies conducted in this research are analysed. The input-output data of the system were first acquired through the experimental studies using National Instruments (NI) data acquisition system. A sinusoidal force was applied to excite the flexible plate and the dynamic response of the system was then investigated. Non-parametric modelling of the system were developed using several artificial intelligent methodologies namely Adaptive Elman Neural Networks (ENN), Backpropagation Multi-layer Perceptron Neural Networks (MLPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of all these methodologies were compared and discussed. Finally, validation and verification of the obtained model was conducted using One Step Ahead (OSA) prediction, mean squared error (MSE) and correlation tests. The prediction ability of the model was further observed with unseen data. The results verified that the MLPNN converge to an optimum solution faster and the dynamic model obtained described the flexible plate structure very well. The non-parametric models of the flexible plate structure thus developed and validated will be used as the representation of the transfer function of the system in subsequent investigations for the development of active vibration control strategies for vibration suppression in flexible structures.

Active vibration control of a flexible beam using system identification and controller tuning by evolutionary algorithm

This paper presents a new approach of proportional-integral-derivative (PID) controller tuning via an evolutionary algorithm that optimally suppresses the vibration of a flexible beam system using a piezoelectric actuator. The system’s dynamic model is identified based on autoregression with exogenous input (ARX) structure using recursive least square. The input-output data were obtained experimentally. This ARX model represents the physical system and is used for the controller optimization process. Evolutionary algorithms such as differential evolution (DE) and genetic algorithms (GA) were applied to optimize and tune the controller parameters offline based on a defined performance index, i.e. mean square error of the vibration signals. The optimum PID parameters were validated experimentally. The performance of PID tuned by DE and GA are compared with conventional PID tuning (using Ziegler Nichols method). Experimental study showed that PID tuned by DE and GA offer a better transient response than the conventional tuning method.

Observer design for active suspension system using sliding mode control

The purpose of this paper is to describe the designing method of an observer for active suspension system by using sliding mode control (SMC). In this regard, for a controlling strategy, the sliding mode has been chosen, and the observer design is used for road profile estimation. It is demonstrated theoretically and by computer simulations that the proposed controller will enhance the road handling performances for the active suspension system compared with the passive suspension system. Comparison between the efficiency of the final controller and linear quadratic regulator embedded in Matlab-Simulink is made. The mathematical modelling and simulation studies proved that SMC with disturbance observer strategy has better performance than LQR control system and the ability to absorb disturbance for SMC is much better than LQR controller.

Active vibration control of flexible beam system using proportional control scheme in finite difference simulation platform

This paper presents the development of dynamic model of flexible beam structure using finite difference method. The simulated model is validated by comparing the resonance modes with the theoretical values. Simple proportional control scheme is used as active vibration control (AVC) algorithm for the system. Simulation study of the control system is done by gradually increasing the proportional gain until it reaches the optimum value. The effectiveness of this control scheme is observed based on its capability to suppress unwanted vibration of the beam. The attenuation of the beam is validated through time and frequency responses. The effect of proportional gain on the dynamic behavior of the actuator is also reported. Simulation is done using LabVIEW graphical programming environment. Results of the study clearly reveal the effectiveness of proportional control scheme in reducing the vibration of the beam.

Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, b...This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

Online monitoring and self-tuning control using pole placement method for active vibration control of a flexible beam

This paper presents the experimental results of online self-tuning pole placement control for active vibration of a flexible beam. The vibration is controlled using a piezoelectric actuator bonded on a flexible beam. An online computer control that runs on a PC-based control and its graphical user interface have been developed in such a way that the user can perform online monitoring and manipulation of control parameters of the active vibration control algorithm for a flexible beam system. The parameters of the pole placement controller have been self-tuned based on autoregression with an exogenous terms model of a bonded piezoelectric actuator beam identified via a recursive least square algorithm. A PC-based control system was implemented using a peripheral component interconnect data acquisition card and LABVIEW software. Results show that the online self-tuning pole placement control offers better transient performance over the fixed controller when tested at different tip loads. The control parameters have converged to a new value as the physical parameter of a flexible beam is changed.

Application of Multilayer Perceptron and Radial Basis Function Neural Network in steady state modeling of automotive air conditioning system

In this paper, steady-state models of an automotive air conditioning (ACC) are identified based on two different artificial neural networks (ANN) architectures: Multilayer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN). The ANN models are developed with a four-in three-out configuration to simulate the outlet evaporating air temperature, cooling capacity, and compressor power under different combination of input compressor speeds, evaporating air speeds, air temperature upstream of the condenser and evaporator. The required data for the system identification are collected from an experimental bench made up of the original components of an AAC system. Investigations signify the advantage of a RBFNN model over MLPNN in modeling the AAC system.

PID Controller for Idle Speed Control

The performance comparison of PID controllers for idle speed control internal combustion engine (ICE) is presented in this paper. There are three methods of PID tuning applied: heuristic tuning, self-tuning by pole placement, and also iterative learning algorithm (ILA). Prior to that, data collection is gained from four cylinder sparkignition engine modeling and used as the input-output data in the system identification part. The obtained transfer function from RLS technique is used in the development of the control system for the idle speed control by PID controllers. The main objective of the idle speed control is to maintain the idling speed at 650rpm and to reject as quickly as possible the extra load exerted on the engine system during idling mode. Comparing the three methods, it shows that PID tuning by ILA produced better results than heuristic tuning and pole placement.