Irma Wani Jamaludin

Performance comparison of SVPWM and Hysteresis Current Control for Dual motor drives

Dual motor drives fed by single inverter are purposely designed to reduced sizes and cost with respect to single motor drives fed by single inverter. This paper presents the speed responses behavior of Dual Permanent Magnet Synchronous Motor (PMSM) driven base on two different PWM control schemes, which are Space Vector Pulse Width Modulation (SVPWM) and Hysteresis Current Controller. These two techniques are compared for wide range of speed and for variation of load. MATLAB/Simulink has been chosen as the simulation tools. The comparison between SVPWM controller and Hysteresis Current Controller for Dual PMSM fed by single inverter is presented. Both techniques show satisfactory speed regulation for a wide range of speed either with load, no load or variation of load.

Simulation single phase shunt active filter based on p-q technique using MATLAB/Simulink development tools environment

This paper presents a single phase shunt active power filter based on instantaneous power theory. The active filter will be connected directly to utility in order to reduce THD of load current, in this case the utility is TNB. The instantaneous power theory also known as p-q theory is used for three phase active filter and this paper proves that the p-q theory can also be implemented for single phase active filter. Since the system has only single phase signal for both voltage and current, thus the dummy signal with 120 º different angels must be generated for input of the p-q theory. The p-q technique will generate six signals PWM for switching IGBT, but only two of the signals will be used to control the switching IGBT. The simulation results are on MATLAB/Simulink environment tools presented in order to demonstrate the performance of the current load on single phase shunt active power filter.

N4SID and MOESP subspace identification methods

Multivariable Output Error State Space (MOESP) and Numerical algorithms for Subspace State Space System Identification (N4SID) algorithms are two well known subspace identification techniques discussed in this paper. Due to the use of robust numerical tools such as QR decomposition and singular value decomposition (SVD), these identification techniques are often implemented for multivariable systems. Subspace identification algorithms are attractive since the state space form is highly suitable to estimate, predict, filters as well as for control design. In literature, there are several simulation studies for MOESP and N4SID algorithms performed in offline and online mode. In this paper, order selection, validity and the stability for both algorithms for model identification of a glass tube manufacturing process system is considered. The weighting factor α, used in online identification is obtained from trial and error and particle swarm optimization (PSO). Utilizing PSO, the value of α is determined in the online identification and a more accurate result with lower computation time is obtained.

Thermal analysis on PCB using Galerkin approach

This paper presents the analysis of temperature rise on PCB. Numerical solutions using Galerkin approach that is one of Finite Element Method (FEM) was used to study and analyze the temperature rise on PCB. Then, an algorithm has been developed using MATLAB. The PCB with four different width of copper is supplied with different range of direct current (DC) from 0.5 to 5 Amperes to capture the data by using FLIR i5 Infrared Thermal Imager. The data then compared with numerical result and obtain an error. In order to minimize the error, an analysis is done based on Galerkin approach with heat transfer film coefficient obtained from the experiment. The numerical results showed good agreement with the experimental results and it can be proved that the algorithm developed based on Galerkin approach can be applied to the thermal analysis on PCB.

Development of an automatic parallel parking system for nonholonomic mobile robot

This paper depicts the development of backward automatic parallel parking system for nonholonomic mobile robot. The configuration of the system consists of ultrasonic sensor, rotary encoder, controller, and actuators. The path planning algorithm is developed based on the data acquired from the sensor. The proposed idea of the path planning is based on the geometrical equations in which the needed information is referring to the distance between the mobile robot and the adjacent object. The ultrasonic sensor and rotary encoder respectively used to detect parking area and measure the detected space. A PIC32MX360F512L microcontroller is used in order to generate the algorithm and control the movement of the mobile robot. System implementation is briefly described to depict the system as a whole. Experimental results are presented to demonstrate and validate effectiveness of the technique used.

Comparison of NARX neural network and classical modelling approaches

Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.

Data-Driven Subspace Predictive Control for a MIMO System

Subspace-based Model Predictive Control (SMPC) is a combination of a result in subspace system identification with Model Predictive Control (MPC) method. Particularly, it uses the subspace linear predictor equation to predict the future value of the system in the MPC implementation, instead of the usual state-space representation. The recursive subspace identification which updates the estimation of the extended observability matrix online is presented here for a Multi Input-Multi Output (MIMO) system specifically for a nonlinear Biological Waste Water Treatment Process. Givens rotation is applied for recursive updating of QR decomposition of a matrix in this SMPC. In SMPC, the need to have an explicit state-space representation of the system is abolished, resulting in a control algorithm that performs system identification and controller design in a single simultaneous step. Additionally, SMPC algorithm will inherit the numerical robustness typical of subspace-based methods thus giving us an easily deployable control implementation in adaptive framework.

Design of a High Force Density Tubular Linear Switched Reluctance Actuator (TLSRA) Without Permanent Magnet

A novel tubular linear switched reluctance actuator (TLSRA) without permanent magnet that has 7:7 stator-to-mover pole pairs ratio is presented in this paper. A detailed analysis of the effect of mover parameters on the performances of proposed TLSRA is presented to determine the optimized actuator parameters. As comparison, the performances of the conventional TLSRA with 7:5 stator-to-mover pole pairs ratio is also designed and compared with the proposed TLSRA using the identical dimensions. The differences between the proposed and conventional TLSRA is number of available working pole pairs. The proposed TLSRA has four working pole pairs for a three phases actuator instead of two working pole pairs for conventional TLSRA. The additional working pole pairs in the proposed TLSRA exhibit force improvement, approximate two times higher compared to the conventional TLSRA. The static force characteristics for the proposed TLSRA is calculated and computed by using the three-dimensional finite element method (FEM) with ANSYS Maxwell software.

Optimization design and analysis of an underactuated dexterous robotic hand system

This paper presents the optimization of three-dimensional (3D) Printed Underactuated Dexterous Robotic Hand (UDRH) design and the analysis of the UDRH material. The UDRH design consists of three fingers and a thumb at which each finger has two links and two degrees of freedoms (DOF) and each joint of the finger is actuated by the DC Micromotor. The thumb has 3DOF to allow for adduction and abduction of the thumb. The robot hand is designed to be like human hand whereby higher performances in accuracy, stability and consistency can be achieved. The actuators are placed directly in each finger link so that the robot hand has several degrees of freedoms to achieve dexterous grasping. The angular rotation, speed, and torque have to be controlled for each individual joint to enable a more precise control. The structure of the UDRH robot hand is printed by using Flashforge Creator Pro 3D printer machine. In this paper, the optimization and analysis of fused deposition modeling (FDM) printed materials which are Acrylonitrile Butadiene Styrene (ABS), Polylactid Acid (PLA), Nylon, and Polycarbonate (PC) plastic are accomplished by testing for their stress and deformation through finite element analysis by using Solidworks software. From the analysis, the critical points and the parts of the UDRH design that are prone to damage under high force and temperature can be detected. In this study, ABS plastic material is chosen for UDRH prototype due to its properties which are resistance to heat which is 5.47 × 106N/m2, and high melting point of 210°C compared to the other materials.

ANFIS Direct Inverse Control of Substrate in an Activated Sludge Wastewater Treatment System

Nonlinearity of the substrate dynamics is the main inconveniences in its control. Low substrate concentration significantly affects the growth of the microorganisms responsible for treating the wastewater and too high substrate concentration level may lead to drop in the growth rate. Therefore, the control of substrate concentration is highly essential for effective and optimal operation of wastewater treatment plants. However, the control using conventional techniques is quite cumbersome and often impossible. This paper presents adaptive neuro fuzzy inference system (ANFIS) direct inverse control of substrate in an activated sludge system. The performances of the proposed controller are illustrated by tracking the substrate set-points. The simulation results demonstrate that the proposed controller can effectively and accurately control the substrate concentration level. The proposed inverse controller may serve as a valuable control strategy for the wastewater treatment plant.