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This article deals with a novel fractional-order active disturbance rejection control (FADRC) scheme to handle a general integer-order system. The proposed control structure enhances the robustness and performance of the classical active disturbance rejection control, especially for the open-loop gain variation. Based on the Bode's ideal transfer function, an analytical design of a state-feedback control is proposed. The integer-order model of the system to be controlled is first transformed to a noninteger-order one, where the introduced fractional order is a design parameter, which imposes the overshoot of the closed-loop step response. In addition, because the model of the system is transformed to a cascade of integer- and fractional-order integrator (the model is noncommensurate), a commensurate fractional-order extended state observer is proposed to estimate the generalized disturbance. To improve the robustness of the proposed FADRC scheme, an analytical design method of a noncommensurate state-feedback control is proposed. The proposed design method is based on the Bode's ideal transfer function cascaded with an integer-order filter. The proposed FADRC scheme is applied for a pendulum–cart test bed, and the effectiveness and robustness of the proposed control are examined by experiments.

The moon is recognized as an important destination for space science and exploration. To find a satisfactory answer for the mystery of the universe and to make use of the lunar resources for the welfare of human beings, several space agencies are planning manned and unmanned missions on the moon. As a result, the concept of lunar vehicles has begun with an advanced descent scheme to execute a precise and safe landing on the surface of the moon. On the contrary, the energy budget is an important issue for any space mission. To reduce the cost of a space mission, it is necessary to design the vehicle trajectory based on optimized energy resources. Fuel is the main energy in a space mission. Therefore, a fuel-optimized energy generation technique is focused on this research. The design of an algorithm that generates a real-time trajectory for the descent and landing of a lunar probe is critical to ensuring a successful lunar landing mission. A scheme of dual-step trajectory generation for lunar descent is also investigated in this paper. In the algorithm developing process, the thrust-to-mass ratio is considered as a principle variable. Algorithm design along with mathematical modeling and simulation results are described in detail. In addition, the proposed method for generating reference trajectory profiles is also analyzed for fuel consumption and robustness.

Fractional order control (FOC) has received widespread attention in recent years due to its efficient tuning capacity, intuitive concept, and enough flexibility. Again, FOC are known to be robust with the open loop gain in particular. However, the design of FOC demands the knowledge of the model to be modified. But on the other hand, the linear active disturbance control (LADRC) technique is known to be model free controller. In order to achieve the better tracking performance even in uncertain operational conditions by responding timely against external disturbances, these two controllers (FOC and LADRC) are combined to propose a new fractional order LADRC to handle integer order system. Therefore, FOC-based LADRC for heat-flow experiment (HFE) is designed in this paper to track desired trajectories of heat flow. Bode’s ideal transfer function is considered as an orientation model to propose this new controller while using the concept of internal mode control. A better performance of fractional order linear active disturbance control (FO-LADRC) is shown for a very good disturbance rejection capability through simulation and experiments on a heat-flow system.

Recently, deep learning-based models are being extensively utilized for steganalysis. However, deep learning models suffer from overfitting and hyperparameter tuning issues. Therefore, in this paper, an efficient θ -nondominated sorting genetic algorithm- ( θ NSGA-) III based densely connected convolutional neural network (DCNN) model is proposed for image steganalysis. θ NSGA-III is utilized to tune the initial parameters of DCNN model. It can control the accuracy and f-measure of the DCNN model by utilizing them as the multiobjective fitness function. Extensive experiments are drawn on STEGRT1 dataset. Comparison of the proposed model is also drawn with the competitive steganalysis model. Performance analyses reveal that the proposed model outperforms the existing steganalysis models in terms of various performance metrics.

Recently, increasing importance has been given to neural tube defects (NTDs), considered a congenital disability that affects the brain and the spinal cord. NTD occurs because of genetic information passed from parents to children via genes that affect the brain region’s shape or function. Presently, minimum folate carrier (MFC A80G) gene polymorphism and maternal folic acid interactions are associated with NTD. Therefore, we designed and developed a grey wolf optimizer-assisted deep recurrent neural network to predict the association between gene polymorphism and folic acid interaction in NTD. The information of different control families and nuclear family information was collected to examine the process of polymorphism. Moreover, the homozygous mutant type (GG) genotype mothers and folic acid consumption levels were continuously analyzed to identify the link with and risk of NTD. The simulation analysis was performed to evaluate the statistical results in comparison with those obtained using conventional methods. Given the importance of interactions and associations, this research discusses the optimized deep recurrent neural network-based polymorphism process to identify the NTD risk factors with a lower error ratio of 0.015%. We found that MFC-GG genotype polymorphism and folic acid consumptions are essential and effective in lowering the prevalence of NTD among offspring with maximum accuracy rate of 99.5%.

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.

Gait recognition in elderly patient monitoring is a standard process that employs medical healthcare systems, wearable sensors, motion capturing devices, and Information and Communication Technologies (ICT). The patterns of the patient movement are observed at different time instances for identifying the abnormality in gaits to provide better assistance. In this article, a novel Overlapping Gait Pattern Recognition method based on Regression Learning (RL) is introduced. This method classifies the gait pattern based on the direction of movement and angle of deviation of the patient at the initial stage. The analyses of differentiation are performed using RL for identifying the errors and differences in gait patterns through correlation. The errors are recurrently analyzed through different iterates for approximating the recognition accuracy in a reduced time. The classification of patterns through correlation and conditional analysis of the regression helps identify the errors through intense learning and deviation identification. The proposed method is found to achieve better recognition accuracy, fewer error rates, and smaller recognition delays for different gait patterns.

Grid extension from the distribution network is being used to meet the demand for rural electricity all over the world. Due to the extra cost of extending electric lines to rural villages, it is not feasible as the installing and commissioning costs are directly related to several constraints such as distance from the main grid, the land location, utilities to be used, and the size of the approximate load. Consequently, it becomes a challenge to apply technoeconomic strategies for rural electrification. Therefore, considering the above issues of rural electrification through grid power, the renewable energy system can be an attractive solution. This research analyzes different types of loads considering domestic, industrial, and agricultural requirements for a remote village in a developing country like Bangladesh. In this paper, four types of demand scenarios are developed considering the income level of inhabitants of the village. The investigation identifies the optimal scope for renewable energy-based electrification and provides a suitable technoeconomic analysis with the help of HOMER software. The obtained results show that a combined architecture containing solar panel, diesel generator, and battery power is a viable solution and economically beneficial. The optimal configuration suggested for the primary scenario consists of 25 kW diesel generators to fulfill the basic demand. The hybrid PV-diesel-battery system becomes the optimal solution while the demand restriction is removed for secondary, tertiary, and full-option scenarios. Commercial and productive loads are considered in the load profile for these three scenarios of supply. For the primary scenario of supply, the electricity cost remains high as $0.449/kWh. On the other hand, the lowest electricity cost ($0.30/kWh) is obtained for the secondary scenario. Although the suggested optimal PV-diesel-battery might not reduce the cost of electricity (COE) and NPC significantly, it is capable to reduce dependency on diesel utilization. Hence, the emission of carbon is reduced due to less utilization of diesel that helps to minimize the greenhouse effect on the environment.

The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher’s attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher’s attitude and risk level are investigated in the simulation section.

Control engineers and researchers face challenges to design an efficient controller for Ball and beam system due to its nonlinear and unstable properties. This paper investigates two Degrees of Freedom fractional order control of the ball and beam system. It involves the model based method to design controllers’ parameters for the corresponding linear model. The fractional order controllers are specially tuned to have a constant phase margin of the open-loop. This characteristic ensures the robustness of the controllers to the variations of system gain. This paper presents a proper evaluation and comparison between integer and fractional order controllers. Performance of each controller is evaluated in terms of set-point tracking, disturbance rejection, and robustness. The comparison between two controllers is validated through both simulation and experimental results. Strengths and weaknesses in the real-time control are also indicated.

When it comes to design our homes, which have a limited space, we are looking for all available means to successfully "furnish" the house, despite its limited space, so it is imperative to reach the smartest solutions for furnishing this house. These solutions would be conducive to getting the most out of small spaces, and it is preferable to use furniture pieces that are convertible from one function to another so that the furniture pieces perform more than one function, while taking a small space in the house. The multi-use furniture is the result of effective and innovative solutions in the field of interior design to the problem of small living space in light of the population growth and the financial crisis facing the world and the tendency of countries to horizontal expansion which is the acquisition of pieces of furniture that can be transformed from one shape to another or from one job to another in line with the modern style using the art of origami in order to achieve several objectives, including solving the problem of small spaces and making good use of them in addition to adding an aesthetic touch to the interior space, and this is done using the art of origami. Origami is an ancient Japanese art that means forming paper to make three-dimensional models using techniques of folding and carving and combining these techniques with various paper shapes to create composite designs. From the standpoint of emphasizing the role of the interior design in finding solutions for limited spaces, the idea of research came to conclude ideas for multi-use and convertible designs that serve narrow interior spaces using the art of origami.

In this article, chaotic search based constrained equilibrium optimizer algorithm (CS-CEOA) is suggested by integrating a novel heuristic approach called equilibrium optimizer with a chaos theory-based local search algorithm for solving general non-linear programming. CS-CEOA is consists of two phases, the first one (phase I) aims to detect an approximate solution, avoiding being stuck in local minima. In phase II, the chaos-based search algorithm improves local search performance to obtain the best optimal solution. For every infeasible solution, repair function is implemented in a way such that, a new feasible solution is created on the line segment defined by a feasible reference point and the infeasible solution itself. Due to the fast globally converging of evolutionary algorithms and the chaotic search’s exhaustive search, CS-CEOA could locate the true optimal solution by applying an exhaustive local search for a limited area defined from Phase I. The efficiency of CS-CEOA is studied over multi-suites of benchmark problems including constrained, unconstrained, CEC’05 problems, and an application of blending four ingredients, three feed streams, one tank, and two products to create some certain products with specific chemical properties, also to satisfy the target costs. The results were compared with the standard evolutionary algorithms as PSO and GA, and many hybrid algorithms in the same simulation environment to approve its superiority of detecting the optimal solution over selected counterparts.

Dynamic inverse- (DI-) based control technique has been utilized in many applications and proven to be effective. Recently, the inverse dynamic control (IDC), an expansion to the classical DI technique, has been trending with implementation in many areas. It has been proved that IDC is capable of overcoming some limitations in DI-based techniques, particularly in cancellation of useful nonlinearities. This paper extends the implementation of IDC on the positional and speed control of the linear servo cart system. Simulation results further proves that IDC is an effective and robust controller evidently when comparing it with the proportional velocity and lead compensator controller.

Performance evaluation of trajectory tracking for a rotary flexible joint system is demonstrated in this paper. The robust and converse dynamic (RCD) technique is proposed and implemented for this evaluation. This control methodology is of the left inversion type, i.e., the control inputs are obtained by means of plant output error feedback. RCD control encompasses the baseline inverse (BI) control and sliding mode control-based discontinuous control element. The baseline inverse controller enforces the prescribed servo (virtual) constraints that represent the control objectives. The control objectives of the baseline inverse controller are enclosed in the form of servo (virtual) constraints which are inverted using Moore–Penrose Generalized Inverse (MPGI) to solve for the baseline control law. To boost the robust attributes against parametric uncertainties and disturbances, a discontinuous control function is augmented with baseline controller such that semiglobal practical stability is guaranteed in the sense of Lyapunov. To exhibit the effectiveness of RCD control in terms of tracking performance, computer simulations are conducted in Simulink/Matlab environment. Furthermore, the practical implementation is also investigated through a real-time experiment on Quanser’s rotary flexible joint manipulator system. The experimental results obtained by RCD are compared to the conventional sliding mode and fractional-order control techniques.

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient’s lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient’s hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.

Today, there is a large increase in the demand for electricity. The transmission and distribution networks, however, cannot fulfill unbound demands due to the scarcity of resources. Power lines have losses which make the situation more unfavorable for maximum power transfer. Implementing a flexible AC transmission system (FACTS) is one of the best ways to reduce line losses. This paper proposes a FACTS-based method for minimizing the fault current in the system. Switchgear and protection equipment also perform better when this is done. Moreover, due to the reduced fault current of the switched system, a larger amount of power can be transmitted. Static synchronous series compensator (SSSC), static synchronous compensator (STATCOM), and unified power flow controller (UPFC) are evaluated in this case. With STATCOM and UPFC, fault currents are significantly reduced. Furthermore, STATCOM and UPFC can also reduce the fault currents in the power system in addition to voltage regulation and power flow control. A MATLAB/Simulink model is used to evaluate the model’s feasibility.

In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems&mdash, including wind farms&mdash, are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated.

Consumption of formalin with food is a worldwide concern and has become a serious social problem in recent years, especially in the developing societies, which are at a higher risk of danger because of the lack of satisfactory monitoring and policies. The Frequent consumption of formalin causes severe health issues that lead to critical and fatal diseases like chronic cancer. Formalin detection in food is an urgent concern, which is becoming a national issue in developing countries. In this article, a graphene-MoS2 composite 2D sheets with a TiO2-SiO2 nano-layered surface plasmon resonance (SPR) based sensor is presented for formalin detection. Analytical analysis is carried out on the performance parameters of the sensor using MATLAB commercial software. The effect of amalgamation of Graphene-MoS2 with TiO2-SiO2 layers has been studied. The sensitivity and quality factor of the sensor are found to be 98 deg. RIU−1 and 88.89 RIU−1, respectively. This sensor detects the existence of formalin by using attenuated total reflection (ATR) and evaluating the reflectance vs SPR angle and transmittance vs surface plasmon resonance frequency (SPRF) attributes. It is found that, the sensitivity of the traditional SPR based sensor is 70°/RIU, on the other hand, the graphene-MoS2 based sensor improves this sensitivity to 76°/RIU. Finally, a comparative performance study of the traditional SPR sensors and the proposed sensor is also presented.

This paper presents the design approach of Generalized Dynamic Inversion (GDI) for angular position control of SRV02 rotary servo base system. In GDI, linear first order constraint differential equations are formulated based on the deviation function of angular position and its rate, and its inverse is calculated using Moore-Penrose Generalized Inverse to realize the control law. The singularity problem related to generalized inversion is solved by the inclusion of dynamic scaling factor that will guarantee the boundedness of the elements of the inverted matrix and stable tracking performance. Numerical simulations and real-time experiment are performed to evaluate the tracking performance and robustness capabilities of the proposed control law considering nominal and perturbed model dynamics. For comparative analysis, the results of GDI is compared with conventional PID control. Simulation and experimental results demonstrate better angular position tracking for the square-wave and sinusoidal waveforms, which reveals the superiority, and agility of GDI control over conventional PID.

Fractional order control schemes are being actively investigated for various systems. Fractional order concept is incorporated in integral (I), proportional integral (PI), proportional derivative (PD) or proportional integral derivative (PID) controller to investigate the performance of different state variables of the system. These techniques are often used for the purpose of technology transfer but very scanty research has so far been conducted using state space approach. The current investigation is initiated to observe the effect of fractional order controller using state space approach for the system's performance while tracking the position and regulating the speed of a linear servo cart system. Integer order controller based on proportional derivative (PD) approach is also shown for comparison. Simulation responses are presented and analyzed, in this investigation. The superiority of state space approach based fractional order controller is shown in the results. The paper contains a literature review on several control techniques used to control position and speed of a servo-cart system. An over view of mathematical modeling of servo cart system and a description of a proposed fractional controller is presented in this paper. A brief description of integer order control scheme is also presented. Simulated results are compared and discussed for both fractional order controller and integer order controller at the end of this paper.

The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to be minimized. In this paper, a method to enhance the performance of the multiclass support vector machine (MSVM) classifier using the modified cuckoo search (MCS) is proposed. The wavelet packet transform is used to extract the crucial features from the PQD waveforms; these features are utilized as the input data to the classifier. In order to achieve high accuracy, robustness and speed, the MCS optimizes the number of selected features, as well as the penalty factor and slack variable of the MSVM. The proposed combinatorial algorithm (MCS-MSVM) is tested using 31 categories of PQD events; the hypothetical data for these events are generated by the IEEE 1159 Standard parametric equations. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 10, 20, 30 and 40 dB, the accuracies are 98.40, 98.54, 99.14 and 99.64%, respectively. Moreover, the comparative assessment concludes that the proposed method is superior to other heuristics-MSVM classification methods, namely the GA, PSO, differential evolution, harmony search and the conventional cuckoo search. The practical performance of the MCS-MSVM classifier is validated using real-time PQD data of a typical 11-kV underground distribution network, obtained from a particular electrical utility operator. For benchmarking, comparisons are made to 17 most recent PQD classification techniques published in literature. It is found that the proposed method exhibits the highest accuracies and the lowest computation times under ideal and noisy environments.

This paper reviews and evaluates the hardware solutions to mitigate the effect of partial shading for grid-connected photovoltaic (PV) system. It encompasses both the module-level as well as the array-level approaches. The former enhances the energy yield by maximizing the power extraction capability of each individual module. Here, three methods, namely the micro inverter, the power optimizer and the energy recovery circuit are covered. On the other hand, the array-level mitigation—which includes the dynamic and static array reconfigurations optimize the arrangement of the modules to minimize the effect of partial shading on the energy yield. Besides updating the important aspects of the technologies, this paper delves into the economic viability of the solutions, as well as the challenges and future trend. In addition, it benchmarks the performance of the module-level solutions with the system with string inverter using Simulink. It is discovered that if the plant is highly shaded, the net energy gain of the micro-inverter and power optimizer is significant. On the other hand, if the shading is non-existent, employing these devices can be counterproductive (in terms of yield).

A hybrid system combining the energy and attitude control task uses flywheels to store energy and control the attitude of small satellites. Various journal papers containing previous works have recognized this combined architecture. However, due to the uncertainties of on-board performances, it is a challenge in terms of attitude pointing accuracy. Therefore, this paper focuses a full state-feedback control solution to increase the satellite attitude performances. Mathematical model and numerical treatments for full state-feedback control of combined energy and attitude regulating scheme for a small satellite are presented. Simulation results show that an enhanced pitch pointing accuracy can be achieved up to 0.0010 for the proposed control approach. The paper contains an over view of a flywheel architecture along with state space representation of the scheme. A brief description of conventional control scheme is also presented with sample simulated results for comparison. Design of a full state-feedback controller and analysis of simulated results are also presented to show the achieved attitude performances specifically.

Rotary double inverted pendulum is a highly nonlinear complex system and requires a high performance controller for its control. Using gain matrix which is obtained through state feedback technique may create complexity while removing the steady-state error for all states. Incorporating an integral action can be an alternative for these errors. Therefore, a state space–based fractional order controller with fractional integral action is designed and tested on a rotary double inverted pendulum in this article. The fractional integral controller is designed based on Bode’s ideal transfer function. Two degree of freedom is considered for tuning purpose as well. The integer order controller based on the state space approach is also shown for comparison. Simulation and experimental results are presented for both controllers of this rotary double inverted pendulum system.

This paper will illustrate the application of constraint based Robust Generalized Dynamic Inversion (RGDI) control for balancing the Rotary Inverted Pendulum system. The proposed control law is established by hybridizing the equivalent control (conventional GDI) and the robust control. The equivalent control is constructed by formulating the dynamical constraints, based on the attitude deviation functions of the rotary arm and the vertical pendulum. The constraint dynamics are then inverted using dynamically scaled Moore-Penrose Generalized Inverse to solve for the equivalent control. For the robust term, sliding mode control is utilized, that will provide robustness against parametric uncertainties, system nonlinearities, such that semi-global practically stable angular position tracking is guaranteed. The controller's capability is verified through computer simulation and experimental studies on the Quanser's SRV02 Rotary Inverted Pendulum system.

The integration of micro-generation (μG) in distribution networks faces new challenges concerning the technical as well as commercial management. The μG integration in the Low and medium voltage distribution networks has many advantages for the grid operation, such as voltage profiles improvement, power losses reduction, and branches congestion levels reduction. This paper presents a method for guiding continuation power flow simulation of integrating μG on distribution feeders. A base model is designed with variable capacitor bank, μG unit such as PV and Wind generation are integrated. A control method is used to improve the voltage level of each node as well as improving power factor of the systems. The electricity consumption of a university's substation area where commercial, residential and municipal load are presented are modeled using actual data collected from each single residential hall and commercial buildings. This model allows analyzing the power flow and voltage profile along each distribution feeders on continuing fashion for a 24- hour period at hour-by-hour formulation. By dividing the feeder into load zones based on distance from each load node to distribution feeder head, the impact of integration of different μG operation in different condition has been discussed.

This paper presents the balance control design using Robust Generalized Dynamic Inversion (RGDI) for Rotary Double Inverted Pendulum (RDIP) system. The RGDI control comprised of the particular part and the robust control element. The particular part is responsible to enforce the constraint dynamics based on the attitude deviation functions, and is inverted using Moore-Penrose Generalized Inverse (MPGI) to obtain the control law. An additional robust term based on the concept of sliding mode is integrated to enhance the robust characteristics against system nonlinearities, uncertainties and disturbances. The singularity problem is addressed by incorporating a dynamic scale factor in the expression of MPGI. The proposed RGDI control will guarantee semi-global practically stable angular position tracking of the horizontal rotary arm and the stabilization of the two pendulums at the upright position. Numerical simulations are carried out on the RDIP simulator to analyze the controller performance.

As one of the most promising topics in complex control processes, data-driven techniques have been widely used in numerous industrial sectors and have developed over the past two decades. In addition, the fractional-order controller has become more attractive in applied studies. In this article, a fractional integral control is implemented for a rotary flexible joint system. Moreover, an adjusted virtual reference feedback tuning (VRFT) technique is used to tune the fractional-order integrator. In this method, fractional integral control is designed based on state feedback control. Then, VRFT is adjusted and applied to the fractional integral controller. The effectiveness of the proposed adjusted VRFT method is discussed and presented through simulation and experimental results. The tracking performance of the rotary arm and the minimization of the vibration tip is evaluated based on the proposed method. In this article, the comparison of our proposed VRFT fractional scheme is made with the classical state feedback as well as a recently developed state feedback-based fractional order integral (SF-FOI) controller. The current investigations determine the performance improvement of our proposed scheme of comparable structure to the recent SF-FOI, with the introduction of the VRFT to the SF-FOI scheme.

The down-regulation of CD3 protein present on the surface of a T-cell triggers the process of activation. A rapid decrease of CD3 from the surface is required to reach the threshold that is necessary for the continuation of the activation process. Using the flow cytometry technique, it is well established that an amount of down-regulated CD3 proteins recycle and may appear on the surface of T-cell. This article studies the impact of recycled CD3 protein that is recruited into the immune synapse at different rates. The population density of T-cells is distributed over surface protein (CD3) concentration under the framework of population balance models by using a coupled system of PDEs. Such problems need non-conventional population balance modelling so that classical numerical techniques can be applied. A recently developed efficient numerical method is validated against the analytical and numerical solutions. This article is protected by copyright. All rights reserved

Currently the fractional order controller becomes much more popular in automation engineering due its flexible tuning technique of control parameters. Moreover, it may not be possible to remove the steady-state errors for every states by using traditional sate feedback integer control approach. Therefore, in this paper a state feedback based fractional order controller is designed for rotary flexible link system. In this scheme, Bode's ideal transfer function is considered as a bench mark to obtain similar transient response of closed-loop system. Based on these idea, trajectory tracking performance of a rotary flexible link is demonstrated through computer simulation program. Effectiveness and usefulness of the proposed fractional control scheme is shown as well.

In the field of automatic control system design, adaptive inverse is a powerful control technique. It identifies the system model and controls automatically without having prior knowledge about the dynamics of plant. In this paper neural network based adaptive inverse controller is proposed to control a MIMO system. Multi layer perception and back propagation are combinedly used in this investigation to design the NN learning algorithm. The developed structure represents the ability to identify and control the MIMO system. Mathematical derivation and simulation results for both plant identification and control are shown in this paper. Further, to prove the superiority of the proposed technique, performances are compared with recursive least square (RLS) method for the same MIMO system. RLS based adaptive inverse scheme is discussed in this paper for plant identification and control. Also the obtained simulated results are compared for both plant parameter estimation and tracking trajectory performance.

Trajectory optimization is a crucial process during the planning phase of a spacecraft landing mission. Once a trajectory is determined, guidance algorithms are created to guide the vehicle along the given trajectory. Because fuel mass is a major driver of the total vehicle mass, and thus mission cost, the objective of most guidance algorithms is to minimize the required fuel consumption. Most of the existing algorithms are termed as “near-optimal” regarding fuel expenditure. The question arises as to how close to optimal are these guidance algorithms. To answer this question, numerical trajectory optimization techniques are often required. With the emergence of improved processing power and the application of new methods, more direct approaches may be employed to achieve high accuracy without the associated difficulties in computation or pre-existing knowledge of the solution. An example of such an approach is DIDO optimization. This technique is applied in the current research to find these minimum fuel optimal trajectories.