Journal of Modeling and Simulation
Volume:52 Issue: 1, Winter-Spring 2020

In this paper, an extension of back stepping controller for a class of parabolic Partial Differential Equation systems (The Heat Transfer Process) with time-varying spatial boundary condition is fully studied. The PDE system dynamics is first transformed to an exponentially stable target system via a new nonlinear and invertible back stepping transformation with two gain kernel functions. The exponential stability of the closed-loop system is established by using a proper Lyapunov function and knowing the fact that the introduced back stepping transformation has a unique inverse transformation. Finally, numerical simulation is provided to support the effectiveness of the proposed controller.

Fractional order control of electrically driven flexible-joint robots has been addressed in this paper. The controller design strategy is based on the actuators' electrical subsystem considering to voltage saturation nonlinearity. Hence, the knowledge of the actuator/robot dynamics model is not required as it is for many other control strategies. The overall closed-loop system is proven to be stable and the joint position tracking error is uniformly bounded based on the Lyapunov’s stability concept. The satisfactory performance of the proposed control scheme is verified by experimental results.

Engineered systems are man-made systems created to deliver value/service to stakeholders. Many engineered systems should be operated for long period of times within unpredictable and dynamic conditions. Uncertainty can affect system output and its value/service delivery through different ways such as shifts in stakeholder needs and perturbations. It is important for end users to ensure that the system is operable and reliable in unknown environment. Assessing system capability and its ability to do missions under uncertainty conditions is still an important problem for end users. Non-functional properties such as flexibility and changeability are presented and formulated as a response to decrease the impact of dynamic complexities on system value/service delivery. In this paper viability as a good criterion is selected to measure system capability under uncertainty and a 7-step method is developed to measure it. The proposed method has three characteristics: describing the uncertainty in operational environment, analyzing how the uncertainty will affect functional and physical characteristics of the system and finally representing regions in the system architecture that are mostly impacted by operational uncertainties. Design Structure Matrix (DSM) is used to represent relationships between system properties and uncertain scenarios. Finally, an example is presented to show the application of the method.

This paper adopts the antenna selection technique to enhance the covert rate in a wireless communication network comprised of a source, a destination , an external jammer and an eavesdropper. In the covert communication, the level of transmit power is low and hence a source with multiple antennas can be adopted to send the information toward the single antenna destination while concurrently, the jammer transmits an artificial noise signal. For this system model, we consider a scenario where the source is forced to select one or several of its antennas to transmit its confidential information due to its limited RF chains. Furthermore, we consider two different jamming scenarios to support our covert communication: 1) The destination is unableto cancel the jamming signal, 2) The destination can subtract the jamming signal. For such a communication network, our aim is to maximize the covert rate subject to power constraint and covert communication requirement. In the first scenario, the optimization problem is non-convex, and hence, it can be solvedthrough using Difference of Convex function (DC) method while the optimization problem of the second scenario is intrinsically convex. Our numerical results show that the higher the number of selected antennas at the transmitter, the higher the covert rate will be achieved.

Mobile satellite communication experiences various channel state conditions. These channel impairments degrade overall system reliability and bandwidth efficiency. Dynamic link adaptation considers channel variations and adapts the transmission parameters respectively. This paper investigates link adaptation in mobile satellite communications through adaptive coding and modulation scheme. Average spectral efficiency improvement has been obtained by adaptation algorithms while the error probability constraints are met. To further extend our scenario in real world satellite systems, power amplifier nonlinearity is taken into account. Power amplifier nonlinear performance introduces distortion and signal to noise ratio (SNR) degradation. Hence, an optimized adapting procedure is proposed to overcome the resulting impairments. Moreover, propagation delay in satellite links are significantly large which outdates the channel state information (CSI) used for link adaptation decision. Channel states and fading conditions would change considerably in this long round trip time, especially in mobile user scenario. As a result, deploying a prediction method to predict time varying channel for reliable modulation and coding selection is required. The accuracy and performance of physical layer adaptation were improved by implementing channel power prediction, mitigating large round trip time and fast channel variations. Results indicate satisfactory link availability even in severe shadowing states of the channel.

The in-flight simulator is one of the various kinds of aircraft simulators at which a real aircraft provides a platform for simulating the dynamic responses of another aircraft. In this paper, the capability of the in-flight simulation of an aircraft by a host aircraft simulator using the linear quadratic gaussian (LQG) controller is presented. Initially, the maximum likelihood algorithm and the flight test data are used to estimate the aerodynamic derivatives of the guest aircraft and consequently drive its high order aerodynamic model. Then, the linear and nonlinear models of both aircraft in the longitudinal and lateral modes are constructed and the proper LQG controllers are designed for the in-flight simulation of the guest aircraft responses caused by the host aircraft simulator. Next, by applying different commands to the control surfaces of the guest aircraft, its linear and nonlinear dynamic responses are simulated in the longitudinal and lateral modes. Finally, the simulated flight profiles of the guest aircraft are tracked by the host aircraft simulator in the linear and nonlinear schemes. To validate the capability of the LQG controllers for tracking the guest aircraft response, the flight test profile of the guest aircraft is also simulated by the host aircraft simulator.

The use of bank cards has increased significantly in recent years. This has resulted in increasing the probability of internet payment card frauds and has highly imposed losses on customers, institutions and banks. The methods used to detect frauds in this area mainly require a huge volume of historical data. On the other hand, these methods usually work well when there are single bank transactions, which means they only have the ability to detect frauds during single bank transactions and do not reveal fraudulent sequence identification.In this paper, a model is proposed to determine the appropriate sequence length required to evaluate every single customer's spending behavior. Through adding the feature of fraudulent sequence detection in payment cards, the proposed model has been completed. This model automatically creates and updates the Hidden Markov Model of each sequence, and ultimately detects frauds by comparing the Kullback-Leibler divergence between Hidden Markov Model of each sequence. The fraud detection is presented by real semi-supervised payment cards data of an Iranian bank. The obtained F-Score, derived from 7 real fraudulent scenarios created under the supervision of a bank expert, representing 87%. Using the proposed model also leads to a reduction in the fraudulent sequences incidence cost of 81%.

Emergency disaster-relief activities could dramatically reduce injuries and casualties, while routing and scheduling of the relief teams is also considered an important factor in reducing the fatalities. For this reason, in this paper, a new model is proposed for routing rescue teams considering time windows, capacitated and multi-depot vehicles. In this model, additional factors such as availability of relief centers, congestion and service standard for the vehicles. A new parameter has been developed to denote the congestion of each path and id incorporated into the model using the concept of Social Network Analysis (SNA). Finally, the model is solved using a COREI5 8GB system. The model is also implemented using the data obtained from the Roads and Transport Organization and the Iranian Red Crescent Society. The average accuracy of this algorithm was 87% after solving 23 problem samples and improvement of the runtime was 74% in large problems. The model is then applied to the case study of the 2017 earthquake in Kermanshah, Iran. A rescue scenario is generated using the historical data of I.R. Iran Road Maintenance Transportation Organization and the I.R. Relief and Rescue Organization of Red Crescent Society of Iran. In this study, simulations are conducted based on a case study with actual locations.

Although numerous advanced and intelligent controllers have been invented during the last years, classical PID controllers are still of interest to many control engineers and promising candidates for industrial purposes. In this study, the authors study the issue of when a switching controller gains exist to asymptotically track a predefined output profile for second-order linear time-invariant systems. This paper proposes several new criteria to ascertain the existence of the gains of PID controllers so that the output be in predefined values at special times. Since permitted output range is calculated for the nonzero initial values, we can switch between several PID gains in several steps to have a specific variation of output with time. In this way, several desired targets can be achieved without any compromising. It is the main goal of this paper. In fact, a scenario is considered for the output which determines its values in several times and these times generate time intervals over which variable gains are applied to the system. Requirements for tracking can be readily achieved with choosing the output value according to the criteria. It is evaluated by several simulation examples, which demonstrate that the proposed approach works well to obtain PID controller parameters in a guaranteed way.

The aim of pansharpening is to fuse the low resolution multispectral (MS) image with the high resolution panchromatic (PAN) image to provide a synthesized MS image with high resolution. One of the main approaches for pansharpening is the multi-resolution analysis (MRA). It is generally successful in transform of spectral information. But, it often results in spatial distortion in the fused product. To deal with this problem, a morphological profile based multi-scale transform (MP-MST) is proposed in this paper which utilizes the good characteristics of morphological filters for reduction of spatial redundancies in the pansharpened image. More efficient approximate image and detail image are achieved from the MS and PAN images by applying the closing and opening by reconstruction operators, respectively. Different spatial structures with different sizes are extracted through considering a range of structuring elements sizes. The performance of the proposed MP-MST methods is compared to MST ones by doing experiments on three different remote sensors GeoEye, QuickBird and IKONOS. The experiments show the superior performance of MP-MST method compared to MST in terms of various qualitative assessments. The visual comparison is also investigated. The proposed MP-MST methods solve the problem of noise and redundant spatial information in the pansharpened images significantly.

This paper is concerned with the eradication of tumor cells in the human body by defining an optimal protocol using a polynomial approximation technique for the injection of chemotherapy drugs. The dynamics of the system are described based on immune-oncology. Variation of host, tumor, and immune cells’ populations are studied in the model during the injection of the chemotherapeutic drugs. The objective is the minimization of cancerous cells' average population by minimum drug injection to avoid the destructive side-effects of these chemotherapeutic substances. It should be done by stabilizing the population of host and immune cells around a free-tumor desirable health condition. This optimization problem by considering the nonlinear model of the system makes applying nonlinear optimal control inevitable. Solving Hamilton-Jacobi-Bellman (HJB) nonlinear partial differential equation (PDE) for the system is put into our perspective to cope with this problem. Since the dynamics of the system are not polynomial, it comprises fractional terms, this PDE cannot be solved straightforwardly. We take advantage of the power series expansion technique to approximate the solution of the PDE with satisfactory accuracy. Finally, a series of simulations are carried out to prove the capability of the controller in terms of robustness and sensitivity, increasing convergence rate for the elimination of cancerous cells, and enlargement of the domain of attraction.

The increasing popularity of cloud computing environments makes task scheduling as a critical problem and a hot research topic. It is necessary to decrease the energy related costs and enhance the lifespan of high performance computing resources used in cloud data centers. Moreover, the high quality of security service is increasingly critical for security-sensitive applications that work with large-scale data files such as bioinformatics. We propose a new task scheduling algorithm that includes: 1) analyzing task execution time based on the load of data centers; 2) modeling the resource utilization; 3) calculating security cost based on the failure probabilities; 4) evaluating power consumption based on the linear model; and 5) analyzing the closeness centrality of data centers to improve data retrieval time. Finally, it designs a fuzzy inference system with five inputs (i.e., total execution cost, resource utilization cost, security cost, energy consumption, and centrality) in order to assign a merit value to each data center for task execution. Cloud is a dynamic environment and there is not accurate information at every moment. Therefore, fuzzy inference is a good choice for predicting the behavior of the system and scheduling decisions. The simulation results indicate that the proposed algorithm obtains superior performances respectively in waiting time, success rate, energy consumption, and degree of imbalance around 14%, 12%, 15%, 11% on average than other similar methods under high load condition. Consequently, the proposed strategy has potentials to enhance the performance of QoS delivery since it can effectively utilize cloud resources.