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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.

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.

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 aim of this article was to determine control strategy for balance control of rotary double inverted pendulum system, which is highly nonlinear and unstable under-actuated system. The complexities involved in rotary double inverted pendulum dynamics make this system a useful engineering test bed to test and verify newly designed controllers. In this article, a constraint-based control approach titled robust generalized dynamic inversion is designed and implemented for robust stabilization of rotary double inverted pendulum system. The robust generalized dynamic inversion control is designed in two stages; in the first stage, constraint differential equations of the controlled state variables are prescribed, which encompasses the control objectives. To enforce the constraint dynamics, the equivalent control is realized by means of Moore–Penrose generalized inversion. To enhance robustness, the switching (discontinuous) control is introduced in second stage, whose design principle is based on classical sliding mode control theory. Finally, the controllers obtained in two stages are augmented to form the resultant robust generalized dynamic inversion control law. The proposed controller ensures robustness along with improved time domain performance regardless of system nonlinearities, uncertainties, and unwanted disturbances. The stability analysis is presented for guaranteeing semi-global asymptotically stable closed loop performance via Lyapunov stability criteria. Numerical simulation and experimental investigations are carried out along with comparative analysis, to demonstrate the effectiveness of robust generalized dynamic inversion control algorithm over other conventional control methods.

Dioctyl phthalate (DOP) is industrially commonly used as a polyvinyl chloride (PVC) plasticiser. As DOP does not form a chemical link with PVC, it migrates from flexible PVC segments into the media in contact, a matter that arose concerns due to its noxious effect. Despite the introduction of several non-DOP-based plasticisers recently, most of these new plasticisers are petroleum derived, which is a non-renewable resource. Accordingly, this research aims to produce a natural-based plasticiser using clean production method. Epoxidised 2-ethylhexyl oleate (E-2-EHO) was produced through an esterification and epoxidation reaction between oleic acid and 2-ethyl hexanol; both reactions occur simultaneously, in the presence of hydrogen peroxide as oxygen donor in a solvent-free environment. Candida antarctica lipase (Novozym 435) was used as a cleaner biocatalyst. Several reaction parameters that affect the synthesis of (E-2-EHO) were analysed using response surface methodology based on full factorial central composite design for four variables. The maximum experimental conversion was 94.2% while the value of the predicted conversion was 95.3%. The operation conditions were a temperature of 65 °C, enzyme load of 4 wt%, alcohol-to-oleic acid molar ratio of 4:1, hydrogen peroxide-to-C=C molar ratio of 0.5:1, molecular sieve/g acid of 0.425 g and reaction time of 2 h. In addition, the plasticising effectiveness of (E-2-EHO) to substitute toxic DOP was studied. Comparison with conventional DOP highlighted that (E-2-EHO) had superior and significantly reduced glass transition temperature (tg) and improved mechanical properties. In the proposed study, (E-2-EHO) was proved to be an efficient substitute to DOP by replacing up to 80% of the total plasticiser. Moreover, the product yield obtained in a short time reaction along with the proven stability of Novozym 435 during operation both showed that this ecofriendly and maintainable alternative is favourable when used in large-scale applications.

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.

Objective: This article analyses elements, characteristic and principle of corporate governance in the public sector. We are going to explore the significance and identify the need for a good governance in public entities. Methods: This study emphasizes comparative analysis of advantages and disadvantages of corporate governance. It also used historical methods for demonstrating the importance of progress in the concept of corporate governance. Several past research journals have been used to present a blend of the need and characteristics of Corporate Governance in the public sector. Through this study, the researchers have made an effort to prove the importance and effectiveness of corporate governance in the public sector. Findings: Delivering public services and satisfying public need is the most important goal of public authorities’ corporate governance. It also has aim to increase shareholders profit through obtaining citizen satisfaction. In addition, satisfying the need of client (citizen) can improve public benefit. Application/Improvements: The main areas of research interest in this still incipient area and it illustrates the ways in which public sector approaches to codes of good practice, corporate ethics and social responsibility differ from their equivalents in the private sector. This study explains and analyses the fundamental concepts of the Corporate Governance of public entities. Also, authors have aim to explain effective governance in the public sector. Keywords: Corporate Governance, Characteristics, Effectiveness, Elements, Principle, Public Sector

In this paper, a 10th order nonlinear pharmacokinetics/pharmacodynamics (PK/PD) model was developed to evaluate the impact of Alzheimer's disease (AD) medications such as galantamine on β-amyloid (βA) aggregates and levels of acetylcholine (ACh) and choline in the presynaptic (compartment 1) and postsynaptic (compartment 2) neurons based on the choline leakage hypothesis. The development is performed by incorporating a one-compartment PD/PK model into the two-enzyme/two-compartment model built by Mustafa et al., (2012a,b). The interaction between medications and βA aggregates is formulated based on a 6-h single oral dose of 2.5-10 mg of drugs which indicated PK/PD changes similar to the experimental findings obtained by Goh et al., (2011). Enhanced cholinergic behavior was observed in terms of a reduction in βA aggregates’ concentrations and increase in the levels of ACh and choline in the pre-synaptic and postsynaptic neurons. The results indicate that therapeutic and pharmacological agents inhibiting βA aggregation represent a likely successful approach to develop future trials, new diagnostic techniques, and medications for AD. This study is helpful in developing experimental animal models to support the development of AD medications.

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.

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.

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.

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.

In the current study, a novel Zeonex based porous core photonic crystal fiber (PC-PCF) is presented for polarization-maintaining and dispersion flattened in the terahertz (THz) region. For minimizing the Effective Material Loss (EML), an array of three rectangular and six triangular air holes are surrounded by hexagonal-shaped cladding. Finite Element Method (FEM) is employed through Comsol V5.3a software to design and examine the essential features of the proposed porous core fiber which revealed that it has an extremely small EML of 0.04 cm−1 at 1.2 THz and has almost zero flattened dispersion of 0.8 ± 0.08 ps/THz/cm in 1.0–1.4 THz frequency spectrum. Moreover, the optimum designing parameters offer an extremely high value of birefringence (0.043 at 1.2 THz). Besides, other major features notably bending loss, effective area, and confinement loss are also found to be precise and relatively low. For effective, adaptable and fitting transmission characteristics, this type of design would lay the foundations for broadband THz radiation wide variety of usage in the THz regime.

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.

In this paper, a fractional order control law is proposed and implemented for the evaluation of trajectory tracking performance of a rotary flexible-joint system. A state feedback based fractional integral control scheme is used in this proposed method. In this scheme, state feedback is responsible for stabilizing the system. The compensator, in series with the fractional integrator leads to obtain a similar closed-loop transient response like Bode’s ideal transfer function. The effectiveness of the proposed controller in tracking and being robust against parameter uncertainties is demonstrated through simulation. In addition, to show the usefulness of the proposed control scheme, the fractional controller is compared to an integer state feedback control by simulation and through experimentation on the Quanser’s rotary flexible-joint system.

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.

This study focuses on determining exergy destruction in a MSF Desalination plant located in Yanbu Saudi Arabia. A non-iterative code in MATLAB is developed to perform mass, energy, and exergy balances around the plant at normal load of distillate production (~ 159 kg/s) at 22.9 °C in the winter and at peak load (~ 200 kg/s) at 35.2 °C in the summer. Exergy flowrates are calculated and exergy destruction around each of the equipment is estimated. An exergy diagram is prepared to indicate equipment with highest exergy destruction. As expected the major exergy destruction occurs at the Heat Recovery Section (74.9%). The simulation results indicate that as the number of stages increase from 25 to 31 the percent exergy destruction drops from 74.9% to 69.2%. The second law efficiency of the plant is 3.22% and 2.84% at normal and peak load respectively. This efficiency is quite low and several measures can be done to improve it by decreasing inlet exergy to the plant and controlling exergy losses during process operation. Two variations in MSF designs i.e., Once Through (MSF-OT) and Brine Mixing (MSF-M) are considered. It is found that the design of MSF-M is a viable option to minimize exergy losses.

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.

In this paper, the effect of β-amyloid (Aβ) oligomer aggregates on Acetylcholine (ACh) neurocycle of human brain and its relation to Alzheimer's disease (AD) are investigated through a two-enzyme/two-compartment (2E2C) diffusion mathematical model whereby compartment 1 represents the presynaptic neuron while compartment 2 represents the postsynaptic neuron and postsynaptic cleft. It is found that Aβ aggregates induce choline leakage from the presynaptic neurons (compartment 1) leading to a reduction in choline content in both compartments. The lowering in choline levels in each compartment is promoted with the increase in the inlet Aβ oligomers which create more channels and pores in the presynaptic membrane. In addition, it is found that both the rates of ACh synthesis and hydrolysis were affected negatively by the increase in the levels of Aβ oligomers. Furthermore, the levels of ACh in both compartments decreased while the level of Aβ oligomers increased. However, the acetate concentration in compartment 1 increased but the acetate level in compartment 2 decreased. These results are compatible with the low levels of both ACh and choline in the neuron tissues of Alzheimer's disease (AD) brains through choline leakage hypothesis. Also, the model shows the significant effect of inhibiting Aβ aggregation as a promising therapeutic mechanism to mitigate cholinergic diseases. The model suggests that Aβ oligomers exert neuroinflammatory and toxic effect against the ACh neurocycle. The paper suggests that development of therapeutic agents capable of inhibiting toxic Aβ aggregations and maintaining choline substrates in neurons is necessary to provide a neuroprotective effect, cholinergic transmission, and maintain reasonable levels of ACh.

In this paper, an original model-based analytical method is developed to design a fractional order controller combined with a Smith predictor and a modified Smith predictor that yield control systems which are robust to changes in the process parameters. This method can be applied for integer order systems and for fractional order ones. Based on the Bode's ideal transfer function, the fractional order controllers are designed via the internal model control principle. The simulation results demonstrate the successful performance of the proposed method for controlling integer as well as fractional order linear stable systems with long time delay.

In this paper, fractional-order controller designs for integer first-order plus time delay systems are investigated. Based on Bode’s ideal transfer function as a reference model, a new structure of the fractional-order controller is proposed. The internal model control principle is used to design the controllers. The effectiveness of the controllers is demonstrated through simulations and their efficiency is validated through experimentation on a heat flow platform.

State feedback technique through a gain matrix has been a well-known method for pole assignment of a linear system. The technique could encounter a difficulty in eliminating the steady-state errors in some states. Introducing an integral element can effectively eliminate these errors. State feedback with fractional integral control is proposed, in this work, for pole placement of a linear time invariant system. The proposed method yields simple gain formulae. The paper presents the derivation of the design formulae. The method is applied to stabilise an inherently unstable inverted pendulum-cart system. Simulation and experimental results show the effectiveness of the proposed method for set-point tracking, disturbance rejection and stabilising the inverted pendulum. Comparison with the results obtained from applying Achermann’s formula is also presented.

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.

This paper analyzes the optimum orientation and tilt angle effects on photovoltaic (PV) module performance in Jeddah, Saudi Arabia. This analysis will begin with the description of solar radiation and tilt angle concepts. These concepts are then used to investigate the performance of PV modules by determining the power output while varying the tilt and azimuth angles. Several azimuth and tilt angles have been analyzed for monthly, bimonthly, trimonthly, quarterly, six-monthly, and yearly periods to determine how much power output can be generated and to select the best orientation angles for the PV system. The results indicate that the highest monthly average output power of the PV is 0.225 kW, obtained in June at the 0° tilt surface of the 1-kW PV panel. However, bimonthly, trimonthly, quarterly, six-monthly, and yearly adjustments result in lower power output, with yearly adjustment giving the lowest power. Therefore, the tilt angle should always be adjusted in the shortest time possible in the interest...