Evolutionary low frequency steering vibration control towards human spine

This paper demonstrated a simulation study of an active vibration control using particle-swarm optimisation based proportional, integral and derivative (PSO-PID) control scheme to suppress steering vibration towards human vertebrae impact. The vertebrae dynamic model is identified based on grey-box modelling technique. This technique combines physical behaviour information of the spine via mathematical model and robust black-box model of the spine with added vehicle speed variation. The performance of PID-PSO control scheme is validated and compared with the conventional PID control scheme. PSO falls under the umbrella of evolutionary algorithms which is used to optimise and tune the PID controller parameters (Kp, Ki and Kd) based on a predefined performance index. The main objective is to minimise the mean square error (MSE) of the vibration signal. The optimum PSO-PID parameters are then used to suppress vibration induced by steering vehicles to the spine. This study showed that PSO-PID is better tuned than the conventional tuning method in terms of transient response

System modelling of rocker-bogie mechanism for disaster relief

In December 2014, the east coast of Malaysia faced a massive flood from heavy downpour, leading to huge flood damage and caused irreparable loss to life and property. The flood carries the debris, soil and trees along their path, damaging the road and building structure, leaving the road become uneven. This situation gives difficulty to task force bearing aids during the post disaster management. This paper proposed an intelligent inclined motion control of an amphibious vehicle while moving on uneven terrain surface

ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS

Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up that resembles the behaviour of a helicopter, namely twin rotor multiinput multi-output system (TRMS) is used as an experimental rig in this research. An adaptive neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm is developed in search for non-parametric model for the TRMS. The antecedent parameters of the ANFIS are optimized by a PSO algorithm and the consequent parameters are updated using recursive least squares (RLS). The results show that the proposed technique has better convergence and better performance in modeling of a nonlinear process. The identified model is justified and validated in both time domain and frequency domai

MLP and Elman recurrent neural network modelling for the TRMS

This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology

Dynamic nonlinear inverse-model based control of a twin rotor system using adaptive neuro-fuzzy inference system

A dynamic control system design has been a great demand in the control engineering community, with many applications particularly in the field of flight control. This paper presents investigations into the development of a dynamic nonlinear inverse-model based control of a twin rotor multi-input multi-output system (TRMS). The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. A model inversion control with the developed adaptive model is applied to the system. An adaptive neuro-fuzzy inference system (ANFIS) is augmented with the control system to improve the control response. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered in order to evaluate the tracking properties and robustness capacities of the inverse- model control technique

Computer aided medical diagnosis for the identification of Malaria parasites

Interest in digital image processing methods stems from two principal application areas which comprises of improvements in pictorial information for human interpretation. This paper presents one of the applications of digital image processing in artificial intelligence particularly in the field of medical diagnosis system. Currently in Malaysia the traditional method for the identification of Malaria parasites requires a trained technologist to manually examine and detect the number of the parasites subsequently by reading the slides. This is a very time consuming process, causes operator fatigue and is prone to human errors and inconsistency. An automated system is therefore needed to complete as much work as possible for the identification of Malaria parasites. Digitized microscopic images of thin blood smear specimens are used in this project. The endeavor is to develop a software where the end user can use a computer aided medical diagnosis system via graphical user interface whereby the number of existed Malaria parasites will be counted. The technique used is Digital Image Processing whilst the main programming language used is C++ programming which permits portability of the program so as to provide easy future software expansion. The integration both soft computing tools in this project has been successfully designed with the capability to improve the quality of the image, analyze and classify the image as well as calculating the number of Malaria parasites

Inverse model based control for a twin rotor system

The use of active control technique has intensified in various control applications, particularly in the field of aircraft systems. A laboratory set-up system which resembles the behaviour of a helicopter, namely twin rotor multi-input multioutput system (TRMS) is used as an experimental rig in this research. This paper presents an investigation using inverse model control for the TRMS. The control techniques embraced in this work are direct inverse-model control, augmented PID with feedforward inverse-model control and augmented PID with feedback inverse-model control. Particle swarm optimization (PSO) method is used to tune the parameter of PID controller. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered. The proposed inverse model based controller is shown to be capable of handling both systems dynamic as well as rigid body motion of the system, providing good overall system performance

Torsional vibration reduction with augmented inverse model-based controller in wind turbine drivetrain

Wind energy has shown promising advantages in reducing the greenhouse effect by minimizing carbon dioxide emissions to improve earth climate. Wind turbine which falls under the umbrella of renewable energy family promises cleaner environment while generating electricity from wind energy with no burnt fossil fuel. However, it portrays challenges in terms of high operating cost due to component failure. Thus this paper discusses on mitigating one of the problems related to wind turbine failure, the torsional vibration reduction in drive train. A generator torque control is investigated together with the particle swarm optimization technique in search for accurate parameters of the controller. This control strategy is a solution to low wind speed areas especially around South East Asian region. An augmented inverse model-based controller and band pass filter is proposed to obtain vibration attenuation at the dominant mode. The modelling endeavor is firstly obtained via particle swarm optimization search capability to obtain an accurate transfer function of the inverse model. A band pass filter (BPF) is then augmented with the inverse model as controller for torsional vibration suppression. Results have shown favorable comparison between the proposed and conventional methods in terms of vibration attenuation level