New control for two mass positioning system using nominal characteristic trajectory following controller

In this study, a nominal characteristic trajectory following (NCTF) controller for point-to-point (PTP) positioning system is introduced for two mass systems and its performance is evaluated.The NCTF controller consists of a nominal characteristic trajectory (NCT) and a compensator. The objective of the NCTF controller is to make the object motion follow the NCT and end at its origin.Therefore, the NCT is used as an intended object motion and the compensator is used to make the motion of the controlled object follow the NCT.The NCTF controller is designed based on a simple open-loop experiment of the object and no information except the NCT is necessary for controller design. The effectiveness of the NCTF controller is evaluated and discussed through simulations.The effect of the design parameters on the robustness of the NCTF controller to inertia and friction variations is evaluated and the influence of saturation on the positioning performance is examined

Singular Value Decomposition (SVD) Based Orthogonal Transform Approach for Earth's Electric Field Signal Processing

The Earth's electric field signal is generated from the released energy through a sudden dislocation of the segment in the earth's crust. Many researchers have reported the use of parametric modeling technique for earth's electric field signal processing. The existing earth's electric signal processing based on parametric modeling technique has suffered from the noise. Therefore, the effective earth's electric field signal processing is necessary in order to process the signal with better performance for the identification. Singular value decomposition (SVD) based parametric modeling technique is applied as feature extraction technique to the Earth's electric field signal. The projection of excitation signal on the right eigenvector of the LPC filter impulse response matrix is involved in this technique. The combination of SVD-based parametric modeling technique has perfectly classified the significant Earth's electric field data prior to the earthquake and the Earth's electric field data on the normal condition after the polynomial kernel function is applied

Time Domain Feature Extraction Technique for earth's electric field signal prior to the Earthquake

Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earth's electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earth’s electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms= 5R (5R), while 7 of them were recorded with magnitudes greater than Ms= 6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earth’s electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earth's electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system

Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window

Seismic electric signal (SES) is one of features for predicting earthquakes (EQs) because of its significant changes in the amplitude of the signal prior to the earthquake. This paper presents detailed analysis of SES recorded prior to earthquake that occurred in Greece in the period from January 1, 2008 to June 30, 2008. During this period of time 5 earthquakes were recorded with magnitudes greater than 6R. In this analysis STFT involving adaptively sliding window technique is used, in which Hamming and rectangular window functions are applied and compared. The comparison shows that Hamming window gives better results in analyzing the first significantly changes of SES prior to the EQ. The application of Hamming window resulted in less rippled spectrum shape which is more suitable to be used in characterizing the SES

Negative imaginary theorem with an application to robust control of a crane system

This paper presents an integral sliding mode (ISM) control for a case of negative imaginary (NI) systems. A gantry crane system (GCS) is considered in this work. ISM is a nonlinear control method introducing significant properties of precision, robustness, stress-free tuning and implementation. The GCS model considered in this work is derived based on the x direction and sway motion of the payload. The GCS is a negative imaginary (NI) system with a single pole at the origin. ISM consist of two blocks; the inner block made up of a pole placement controller (NI controller), designed using linear matrix inequality for robustness and outer block made up of sliding mode control to reject disturbances. The ISM is designed to control position tracking and anti-swing payload motion. The robustness of the control scheme is tested with an input disturbance of a sine wave signal. The simulation results show the effectiveness of the control scheme

Maximum power extraction strategy for variable speed wind turbine system via neuro-adaptive generalized global sliding mode controller

The development and improvements in wind energy conversion systems (WECSs) are intensively focused these days because of its environment friendly nature. One of the attractive development is the maximum power extraction (MPE) subject to variations in wind speed. This paper has addressed the MPE in the presence of wind speed and parametric variation. This objective is met by designing a generalized global sliding mode control (GGSMC) for the tracking of wind turbine speed. The nonlinear drift terms and input channels, which generally evolves under uncertainties, are estimated using feed forward neural networks (FFNNs). The designed GGSMC algorithm enforced sliding mode from initial time with suppressed chattering. Therefore, the overall maximum power point tracking (MPPT) control is very robust from the start of the process which is always demanded in every practical scenario. The closed loop stability analysis, of the proposed design is rigorously presented and the simulations are carried out to authenticate the robust MPE.Izhar Ul Haq, Qudrat Khan, Ilyas Khan, Rini Akmeliawati, Kottakkaran Soopy Nisar, and Imran Kha

Investigation of the characteristics of geoelectric field signals prior to earthquakes using adaptive STFT techniques

An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to predict earthquakes (EQs) because of significant changes in the amplitude of the signal prior to an earthquake. This paper presents a detailed analysis of geoelectric field signals of earthquakes which occurred in 2008 in Greece. In 2008, 12 earthquakes occurred in Greece. Five of them were recorded with magnitudes greater than Ms = 5R (5R), while seven of them were recorded with magnitudes greater than Ms = 6R (6R). In the analysis, the 1st significant changes of the geoelectric field signal are detected. Then, the signal is segmented and windowed. The adaptive short-time Fourier transform (adaptive STFT) technique is then applied to the windowed signal, and the spectral analysis is performed thereafter. The results show that the 1st significant changes of the geoelectric field prior to an earthquake have a significant amplitude frequency spectrum compared to other conditions, i.e. normal days and the day of the earthquake, which can be used as input parameters for earthquake prediction

Preload-based Starling-like control of rotary blood pumps: an in-vitro evaluation

Due to a shortage of donor hearts, rotary left ventricular assist devices (LVADs) are used to provide mechanical circulatory support. To address the preload insensitivity of the constant speed controller (CSC) used in conventional LVADs, we developed a preload-based Starling-like controller (SLC). The SLC emulates the Starling law of the heart to maintain mean pump flow ([Formula: see text]) with respect to mean left ventricular end diastolic pressure (PLVEDm) as the feedback signal. The SLC and CSC were compared using a mock circulation loop to assess their capacity to increase cardiac output during mild exercise while avoiding ventricular suction (marked by a negative PLVEDm) and maintaining circulatory stability during blood loss and severe reductions in left ventricular contractility (LVC). The root mean squared hemodynamic deviation (RMSHD) metric was used to assess the clinical acceptability of each controller based on pre-defined hemodynamic limits. We also compared the in-silico results from our previously published paper with our in-vitro outcomes. In the exercise simulation, the SLC increased [Formula: see text] by 37%, compared to only 17% with the CSC. During blood loss, the SLC maintained a better safety margin against left ventricular suction with PLVEDm of 2.7 mmHg compared to -0.1 mmHg for CSC. A transition to reduced LVC resulted in decreased mean arterial pressure (MAP) and [Formula: see text] with CSC, whilst the SLC maintained MAP and [Formula: see text]. The results were associated with a much lower RMSHD value with SLC (70.3%) compared to CSC (225.5%), demonstrating improved capacity of the SLC to compensate for the varying cardiac demand during profound circulatory changes. In-vitro and in-silico results demonstrated similar trends to the simulated changes in patient state however the magnitude of hemodynamic changes were different, thus justifying the progression to in-vitro evaluation.Mahdi Mansouri, Shaun D. Gregory, Robert F. Salamonsen, Nigel H. Lovell, Michael C. Stevens, Jo P. Pauls, Rini Akmeliawati, Einly Li

Non-Halal biomarkers identification based on Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography-Time of Flight Mass Spectroscopy (GC-TOF MS) techniques

Consumption of meat from halal (lawful) sources is essential for Muslims. The identification of non-halal meat is one of the main issues that face consumers in meat markets, especially in non-Islamic countries. Pig is one of the non-halal sources of meat, and hence pig meat and its derivatives are forbidden for Muslims to consume. Although several studies have been conducted to identify the biomarkers for nonhalal meats like pig meat, these studies are still in their infancy stages, and as a result there is no universal biomarker which could be used for clear cut identification. The purpose of this paper is to use Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography-Time of Flight Mass Spectroscopy (GC-TOF MS) techniques to study fat of pig, cow, lamb and chicken to find possible biomarkers for pig fat (lard) identification. FTIR results showed that lard and chicken fat have unique peaks at wavenumbers 1159.6 cm-1, 1743.4 cm-1, 2853.1 cm-1 and 2922.5 cm-1 compared to lamb and beef fats which did not show peaks at these wavenumbers. On the other hand, GC/MS-TOF results showed that the concentration of 1,2,3-trimethyl-Benzene, Indane, and Undecane in lard are 250, 14.5 and 1.28 times higher than their concentrations in chicken fat, respectively, and 91.4, 2.3 and 1.24 times higher than their concentrations in cow fat, respectively. These initial results clearly indicate that there is a possibility to find biomarkers for non-halal identification.Gunawan Witjaksono, Irwan Saputra, Marsad Latief, Irwandi Jaswir, Rini Akmeliawati, and Almur Abdelkreem Saeed Rabi

Fuzzy-based temperature and humidity control for HVAC of electric vehicle

Vehicles are the people’s main means of transportation and thermal comfort in the vehicle cabin plays an important role. The heating, ventilating, and air-conditioning (HVAC) operation system in the vehicle cabin decides the degree of comfort, traffic safety as well as health of the occupants. The challenge of designing the HVAC control is to automatically achieve the thermal comfort, regardless of time varying weather conditions. Since most HVAC systems are complex with nonlinearity, distributed parameters, and multivariable therefore many classical controls do not necessarily yield a satisfactory control performance. This paper presents the development of temperature and humidity control strategy for HVAC based on Fuzzy Logic Control (FLC). The goal of the FLC-based temperature control is to satisfy the convergence and equilibrium property. The simulation test results have been shown a satisfactory to control the temperature and humidity