Muhammad Salihin Saealal

H ∞ controller with graphical LMI region profile for gantry crane system

This paper presents investigations into the development of H∞ controller with pole clustering based on LMI techniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional pole placement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system with respect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The results of the response with the controllers are presented in time domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control techniques is discussed and presented.

Single Input Fuzzy Controller With Command Shaping Schemes For Double-Pendulum Type Overhead Crane

This paper presents investigations into the development of composite control schemes for trajectory tracking and anti‐sway control of a double‐pendulum‐type overhead crane (DPTOC) system. A nonlinear DPTOC system is considered and the dynamic model of the system is derived using the Euler‐Lagrange formulation. The proposed method, known as the Single Input Fuzzy Logic Controller (SIFLC), reduces the conventional two‐input FLC (CFLC) to a single input single output (SISO) controller. The SIFLC is developed for position control of cart movement. This is then extended to incorporate input shaping schemes for anti‐swaying control of the system. The input shapers with different mode selection are designed based on the properties of the system. The results of the response with the controllers are presented in time and frequency domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specifications in comparison to SIFLC con...

Performance evaluation of vector evaluated gravitational search algorithm II

This paper presents a performance evaluation of a novel Vector Evaluated Gravitational Search Algorithm II (VEGSAII) for multi-objective optimization problems. The VEGSAII algorithm uses a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. The results shows that the VEGSA is outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.

Real-Time Nonlinear Complementary Filter on SO(3) for Attitude Estimation of Small-Scale Aerial Robot

This paper presents the real-time implementation of a powerful nonlinear complementary filter on special orthogonal group of rotation matrices, called as NCF SO(3) for attitude estimation. It fuses the raw data from accelerometers, magnetometer, and gyroscopes sensors to get reliable real-time attitude estimation. Gyroscopes is used as the main sensor for attitude estimation and another two sensors are used to correct drift error of gyroscopes. In this paper, the performance of NCF SO(3) is explored on performance in highly dynamic manoeuvres in real-time. Real-time experiments were conducted to compare its performance with conventional Extended Kalman Filter (EKF) to exploit the positive features of NCF SO(3) for small-scale aerial robot with limited on-board processor memory cases. The experimental results show the proposed real-time filter has excellent estimated attitude data and can reduce the computational cost, compared to EKF. Thus, it is suitable for small-scale aerial robot which has memory limitation of on-board processor.

Forecasting sunspot numbers with Feedforward Neural Networks (FNN) using ‘Sunspot Neural Forecaster’ system

This paper presents the investigations of forecasting performance of different type of Feedforward Neural Networks (FNN) in forecasting the sunspot numbers. Feedforward Neural Network will be used in this investigation by using different learning algorithms, sunspot data models and FNN transfer functions. Simulations are done using Matlab 7 where customized Graphic User Interface (GUI) called ‘Sunspot Neural Forecaster’ have been developed for analysis. A complete analysis for different learning algorithms, sunspot data models and FNN transfer functions are examined in terms of Mean Square Error (MSE) and correlation analysis. Finally, the best optimized FNN parameters will be used to forecast the sunspot numbers.

Classical angular tracking and intelligent anti-sway control for rotary crane system

This paper presents investigations into the development of hybrid control schemes for sway suppression and rotational angle tracking of a rotary crane system. A lab-scaled rotary crane is considered and the dynamic model of the system is derived using the Euler-lagrange formulation. To study the effectiveness of the controllers, initially a classical controller which is collocated proportional-derivative (PD) controller is developed for control of rotary motion. This is then extended to incorporate a non-collocated fuzzy logic controller for control of sway angle of the pendulum. Implementation results of the response of the rotary crane system with the controllers are presented in time and frequency domains. The performances of the control schemes are assessed in terms of level of sway reduction, rotational angle tracking capability and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.