Abstract: Pollution of the Tbilisi atmospheric air with PM2.5 and PM10 in usual and pandemic situations by using the data of 5 stationary observation points is investigated. The values of the statistical characteristic parameters of PM in the atmosphere of Tbilisi are analyzed and trend graphs are constructed. By means of analysis of pollution levels in the quarantine and usual periods the proportion of vehicle traffic in pollution of city is estimated. Experimental measurements of PM2.5, PM10 in the atmosphere have been carried out in different districts of the city and map of the distribution of their concentrations were constructed. It is shown that maximum pollution values are recorded in the city center and along major motorways. It is shown that the average monthly concentrations vary in the range of 0.6-1.6 Maximum Permissible Concentration (MPC). Average daily values of concentration vary at 2-4 days intervals. The distribution of PM10 generated as a result of traffic is numerical modeled. The modeling results are compared with the observation data.
Abstract: The humanoid robot, specifically the NAO robot must be able to provide a highly dynamic performance on the soccer field. Maintaining the balance of the humanoid robot during the required motion is considered as one of a challenging problems especially when the robot is subject to external disturbances, as contact with other robots. In this paper, a dynamic controller is proposed in order to ensure a robust walking (stabilization) and to improve the dynamic balance of the robot during its contact with the environment (external disturbances). The generation of the trajectory of the center of mass (CoM) is done by a model predictive controller (MPC) conjoined with zero moment point (ZMP) technique. Taking into account the properties of the rotational dynamics of the whole-body system, a modified previous control mixed with feedback control is employed to manage the angular momentum and the CoM’s acceleration, respectively. This latter is dedicated to provide a robust gait of the robot in the presence of the external disturbances. Simulation results are presented to show the feasibility of the proposed strategy.
Abstract: To understand the risk for dry storage concrete casks in the cask loading, transfer, and storage phase, the purpose of this research is to establish the probabilistic risk assessment (PRA) analysis methodology for dry storage concrete casks by using SAPHIRE 8 code. This analysis methodology is used to perform the study of Taiwan nuclear power plants (NPPs) dry storage system. The process of research has three steps. First, the data of the concrete casks and Taiwan NPPs are collected. Second, the PRA analysis methodology is developed by using SAPHIRE 8. Third, the PRA analysis is performed by using this methodology. According to the analysis results, the maximum risk is the multipurpose canister (MPC) drop case.
Abstract: In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.
Abstract: The present paper used time-varying parameters which are based on the score function of a probability density at time t to model volatility of saving rate. We used a scaled likelihood function to update the parameters of the model overtime. Our results revealed high diligence of time-varying since the location parameter is greater than zero. Furthermore, we discovered a leptokurtic condition on saving rate’s distribution. Kapetanios, Shin-Shell Nonlinear Augmented Dickey-Fuller (KSS-NADF) test showed that the saving rate has a nonlinear unit root; therefore, it can be modeled by a generalised autoregressive score (GAS) model. Additionally, value at risk (VaR) and conditional tail expectation (CTE) indicate that 99% of the time people in Lesotho are saving more than spending. This puts the economy in high risk of not expanding. Therefore, the monetary policy committee (MPC) of Lesotho should revise their monetary policies towards this high saving rates risk.
Abstract: This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability.
Abstract: In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l2-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.
Abstract: The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller.
Abstract: Unwanted sound reflection can create acoustic discomfort and lead to problems of speech comprehensibility. Contemporary building techniques enable highly finished internal walls resulting in sound reflective surfaces. In contrast, sustainable construction materials using natural and vegetal materials, are often more porous and absorptive. Hemp shiv is used as an aggregate and when mixed with lime binder creates a low-embodied-energy concrete. Cement replacements such as ground granulated blast slag (GGBS), a byproduct of other industrial processes, are viewed as more sustainable alternatives to high-embodied-energy cement. Hemp concretes exhibit good hygrothermal performance. This has focused much research attention on them as natural and sustainable low-energy alternatives to standard concretes. A less explored benefit is the acoustic absorption capability of hemp-based concretes. This work investigates hemp-lime-GGBS concrete specifically, and shows that it exhibits high levels of sound absorption.
Abstract: Millets as a new source of plant protein were not used in food applications due to its poor functional properties. In this study, the effect of high intensity ultrasound (frequency: 20 kHz, with contentious flow) (US) in 100% amplitude for varying times (5, 12.5, and 20 min) on solubility, emulsifying activity index (EAI), emulsion stability (ES), foaming capacity (FC), and foaming stability (FS) of millet protein concentrate (MPC) were evaluated. In addition, the structural properties of best treatments such as molecular weight and surface charge were compared with the control sample to prove the US effect. The US treatments significantly (P
Abstract: In this paper, we have analyzed and compared the performance of various coding schemes. The basic ID prime sequence codes are unique in only dimension, i.e. time slots, whereas 2D coding techniques are not unique by their time slots but with their wavelengths also. In this research, we have evaluated and compared the performance of 1D and 2D coding techniques constructed using prime sequence coding pattern for Optical Code Division Multiple Access (OCDMA) system on a single platform. Analysis shows that 2D prime code supports lesser number of active users than 1D codes, but they are having large code family and are the most secure codes compared to other codes. The performance of all these codes is analyzed on basis of number of active users supported at a Bit Error Rate (BER) of 10-9.
Abstract: In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.
Abstract: This paper presents a model predictive control (MPC)
of a utility interactive three phase inverter (TPI) for a photovoltaic
(PV) system at commercial level. The proposed model uses phase
locked loop (PLL) to synchronize the TPI with the power electric
grid (PEG) and performs MPC control in a dq reference frame. TPI
model consists of a boost converter (BC), maximum power point
tracking (MPPT) control, and a three-leg voltage source inverter
(VSI). The operational model of VSI is used to synthesize the
sinusoidal current and track the reference. The model is validated
using a 35.7 kW PV system in Matlab/Simulink. Implementation
results show simplicity and accuracy, as well as reliability of the
model.
Abstract: In this study, we demonstrate a high-resolution
refractive index sensor based on a Magnetic Photonic Crystal (MPC)
composed of a triangular lattice array of air holes embedded in Si
matrix. A microcavity is created by changing the radius of an air hole
in the middle of the photonic crystal. The cavity filled with gyrotropic
materials can serve as a refractive index sensor. The shift of the
resonant frequency of the sensor is obtained numerically using finite
difference time domain method under different ambient conditions
having refractive index from n = 1.0 to n = 1.1. The numerical results
show that a tiny change in refractive index of Δn = 0.0001 is
distinguishable. In addition, the spectral response of the MPC sensor is
studied while an external magnetic field is present. The results show
that the MPC sensor exhibits a dramatic improvement in resolution.
Abstract: In this paper, a new SMC (Sliding Mode Control)
method with MP (Model Predictive Control) integral action for the
slip suppression of EV (Electric Vehicle) under braking is proposed.
The proposed method introduce the integral term with standard SMC
gain , where the integral gain is optimized for each control period by
the MPC algorithms. The aim of this method is to improve the safety
and the stability of EVs under braking by controlling the wheel slip
ratio. There also include numerical simulation results to demonstrate
the effectiveness of the method.
Abstract: The paper develops a Non-Linear Model Predictive
Control (NMPC) of water quality in Drinking Water Distribution
Systems (DWDS) based on the advanced non-linear quality dynamics
model including disinfections by-products (DBPs). A special attention
is paid to the analysis of an impact of the flow trajectories prescribed
by an upper control level of the recently developed two-time scale
architecture of an integrated quality and quantity control in DWDS.
The new quality controller is to operate within this architecture in the
fast time scale as the lower level quality controller. The controller
performance is validated by a comprehensive simulation study based
on an example case study DWDS.
Abstract: Fast speed drives for Permanent Magnet Synchronous
Motor (PMSM) is a crucial performance for the electric traction
systems. In this paper, PMSM is derived with a Model-based
Predictive Control (MPC) technique. Fast speed tracking is achieved
through optimization of the DC source utilization using MPC. The
technique is based on predicting the optimum voltage vector applied
to the driver. Control technique is investigated by comparing to the
cascaded PI control based on Space Vector Pulse Width Modulation
(SVPWM). MPC and SVPWM-based FOC are implemented with the
TMS320F2812 DSP and its power driver circuits. The designed MPC
for a PMSM drive is experimentally validated on a laboratory test
bench. The performances are compared with those obtained by a
conventional PI-based system in order to highlight the improvements,
especially regarding speed tracking response.
Abstract: In this paper, we present a comparative assessment of
Space Vector Pulse Width Modulation (SVPWM) and Model
Predictive Control (MPC) for two-level three phase (2L-3P) Voltage
Source Inverter (VSI). VSI with associated system is subjected to
both control techniques and the results are compared.
Matlab/Simulink was used to model, simulate and validate the
control schemes. Findings of this study show that MPC is superior to
SVPWM in terms of total harmonic distortion (THD) and
implementation.
Abstract: This paper presents a model predictive control (MPC)
of a utility interactive (UI) single phase inverter (SPI) for a
photovoltaic (PV) system at residential/distribution level. The
proposed model uses single-phase phase locked loop (PLL) to
synchronize SPI with the grid and performs MPC control in a dq
reference frame. SPI model consists of boost converter (BC),
maximum power point tracking (MPPT) control, and a full bridge
(FB) voltage source inverter (VSI). No PI regulators to tune and
carrier and modulating waves are required to produce switching
sequence. Instead, the operational model of VSI is used to synthesize
sinusoidal current and track the reference. Model is validated using a
three kW PV system at the input of UI-SPI in Matlab/Simulink.
Implementation and results demonstrate simplicity and accuracy, as
well as reliability of the model.
Abstract: This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.