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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.

The neurofeedback stimulus content has direct implications for the efficacy of the psychophysiological applications for neurofeedback modality. In particular, enhancements of neurofeedback stimulus content can facilitate improvements in the efficacy of neurofeedback applications in clinical practice. To further elaborate on this aspect, this study introduced systematic enhancements in neurofeedback stimulus content by developing enhanced neurofeedback stimulus content for stress mitigation. The enhancements included the automatic selection of colour of neurofeedback stimulus content environment and instruction messages, as well as, the adaptive selection of threshold of quantitative electroencephalogram (QEEG) features, such as frontal alpha power and frontal alpha asymmetry. The enhancements were based on the outcomes from previous research on the selection of neurofeedback stimulus content for stress mitigation. The improvement in the efficacy of neurofeedback stimulus content was measured statistically by comparing the QEEG and topographic maps. In this study, electroencephalogram data from 20 participants were acquired during multiple sessions of neurofeedback. Analysis of variance and a post hoc test were used to verify the improvement on the efficacy of the neurofeedback application for stress mitigation after the enhancements of the neurofeedback stimulus content; a t–test was used to verify the statistical significance of the stress mitigation by the neurofeedback. The results indicate that the enhancement of the developed neurofeedback stimulus content facilitated stress mitigation during the early sessions of neurofeedback. In conclusion, the efficacy of neurofeedback can be improved using the developed stimulus content with enhancements.

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.

The article applies the robust generalized dynamic inversion control methodology to the problem of stabilizing upright equilibrium configuration of the under-actuated rotary inverted pendulum system while tracking rotary motion of the actuated arm. The proposed robust generalized dynamic inversion control law comprised equivalent and switching control parts. The equivalent control part works to enforce a virtual constraint dynamics of the controlled state variables by means of Moore–Penrose generalized inversion. The switching control part is of the sliding mode type, and it improves robustness against unmodeled system dynamics, parametric uncertainties, and external disturbances. The robust generalized dynamic inversion control design on the linearized model of the under-actuated rotary inverted pendulum is shown to guarantee semi-global asymptotically stable tracking performance. Numerous computer simulations and experiments are conducted on the Quanser rotary inverted pendulum system, revealing that the proposed algorithm has better convergence and tracking performance than conventional sliding mode and generalized dynamic inversion control strategies when both are applied separately.

This paper presents the development of unmanned marine robotic control modelling and control synthesis using a coupled multivariable underactuated nonlinear adaptive U-model approach. The proposed controller was developed using thru an open source robot operating system (ROS) platform. The new adaptive coupled U-model based internal model control (IMC) node was successfully developed and tested. The proposed controller demonstrated the simplicity of the control synthesis process and the implementation of the mathematical algorithm in real-time. The controller was compared with the proven existing GIRONA 500 UUV for real-time performance. The ROS environment provides fast and reliable controller design and development compared to conventional software architecture. Simulation and real-time experiment were conducted using ROS via the GIRONA 500 UUV platform and compared with a PID mission controller. A new ROS node of nonlinear adaptive U-model based IMC was developed using ROS. The results showed good control signal convergence and tracking performance between the plant or system model with the proposed method This research was supported by the Yayasan Universiti Teknologi Petronas-Fundamental Research Grant-Petroleum Resource Fund (YUTP-FRG) funded by PRF under the grant 0015LC0-235, Universiti Teknologi PETRONAS, Malaysia and King Abdulaziz University, Jeddah, Saudi Arabia under the grant 015ME0-066

This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.

Researchers are continuously exploring the potential use of microwave imaging in the early detection of breast cancer. The technique offers a promising alternative to mammography, a standard clinical imaging procedure today. The contrast in dielectric properties between normal and cancerous tissues makes microwave imaging a viable technique for detecting breast cancer. Experimental results are presented in this paper that demonstrate the detection of breast cancer using microwaves operating at 2.4 GHz. The procedure involves antenna fabrication, phantom tissue development, and image reconstruction. Design and fabrication of patch antenna are used in the study, described in detail. The patch antenna pair is used for transmitting and receiving source waves. Tissue mimicking models were developed from paraffin wax and glycerin for the dielectric constants of 9 and 47, respectively, representing the tissue and tumor. Further, AI-based tomographic images were obtained by implementing a filtered back-projection algorithm in the computer. In the results, the presence of the tumor is quantitatively analyzed.

In this study, a new observer for a class of non-linear single input/single output systems is proposed. This observer is based on a modulating function with time/output dependent coordinates transformation which transforms the original system into a like-type observer canonical form. In the new coordinates, the initial conditions are annihilated. This property permits to construct a fast convergent deadbeat observer activated after an time delay chosen in order to avoid the singularity of the transformation at the initial time t = t 0 . The finite-time convergence of the observer is established, i.e. it is shown that the estimation error is bounded in finite time. The proposed observer is used in an output feedback controller design. Using the already established results on the output feedback stabilisation of non-linear input-affine systems, the separation principle is addressed here and the authors show that output feedback controller guarantees under certain conditions the stability of the closed-loop system. Two numerical examples are given to illustrate the effectiveness of the proposed observer-based output feedback controller.

This paper deals with the synchronization of fractional-order chaotic discrete-time systems. First, some new concepts regarding the output-memory observability of non-linear fractional-order discrete-time systems are developed. A rank criterion for output-memory observability is derived. Second, a dead-beat observer which recovers exactly the true state system from the knowledge of a finite number of delayed inputs and delayed outputs is proposed. The case of the presence of an unknown input is also studied. Third, secure data communication based on a generalized fractional-order Hénon map is proposed. Numerical simulations and application to secure speech communication are presented to show the efficiency of the proposed approach.

This paper presents both algorithms and experimental results to solve a distributed rendezvous problem with shortest distance to convex regions. In a multiagent network, each agent is assigned to a certain convex region and has information about only its own region. All these regions might not have an intersection. Through local interaction with their neighbors, multiple agents collectively rendezvous at an optimal location that is a priori unknown to each agent and has the shortest total squared distance to these regions. First, a distributed time-varying algorithm is introduced, where a corresponding condition is given to guarantee that all agents rendezvous at the optimal location asymptotically for bounded convex regions. Then a distributed tracking algorithm combined with a distributed estimation algorithm is proposed. It is first shown that for general possibly unbounded convex regions, all agents rendezvous in finite time and then collectively slide to the optimal location asymptotically. Then it is shown that for convex regions with certain projection compressibility, all agents collectively rendezvous at the optimal location in finite time, even when the regions are time varying. The algorithms are experimentally implemented on multiple ground robots to illustrate the obtained theoretical results.

In distributed networks, the conventional incremental mode of cooperation between the nodes may suffer instability due to two major reasons: (1) large local errors due to accidental problems, and (2) instability due to link failure or noisy link. This causes error propagation through the entire network resulting in divergence. In this research, we propose a novel incremental least mean square algorithm with improved stability by employing convex combination of two filters. Adaptation of one filter is based on the estimate of the adjacent node (incremental type), while that of the other is based on the estimate of the current local node at previous time instant. These two filters are then fused together by using a suitable mixing parameter. An adaptive mixing parameter is further proposed for this convex combination, ensuing dynamic assignment of the weights for the two combining filters. Steady state excess mean square error is derived for the proposed convex combination, and simulations are presented to validate the proposed claims.

The selection of stimulus contents for neurofeedback has direct implications on the efficacy of neurofeedback therapy. In particular, a suitable selection of stimulus contents facilitates the achievement of sustainability during neurofeedback sessions, which has been considered challenging during clinical practice. To further elaborate this point, this research investigates the efficacy of different neurofeedback stimulus contents (audio, video, and games) for stress mitigation. The effectiveness of the contents was measured by statistically comparing quantitative electroencephalogram (QEEG) features, such as alpha power and alpha asymmetry before and after neurofeedback sessions. In addition, the topographic maps of activities were constructed for a visual description. In this study, 29 study participants were recruited, and the EEG data were recorded during multiple neurofeedback sessions. ANOVA and post hoc testing verified the statistical significance of the results of the various stimulus contents, whereas a t-test verified the significance of stress mitigation because of neurofeedback. The results indicate that games exhibit higher effectiveness than audio and video contents for stress mitigation. In addition, the topographic analysis demonstrates the efficacy of neurofeedback training for stress mitigation. In conclusion, the effects of neurofeedback therapy could be enhanced while selecting suitable stimulus contents for neurofeedback protocols.

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include ’No Referable Diabetic Macular Edema Grade (DME)’ and ’Referable DME’ while five categories consist of ‘Proliferative diabetic retinopathy’, ‘Severe’, ‘Moderate’, ‘Mild’, and ‘No diabetic retinopathy’. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.

This paper addresses the problem of robust beamforming for a moving source. To cope with the source movement, the regularization (or diagonal loading) parameter needs to be adjusted regularly. The need for repeatedly finding an adequate value of the regularization parameter increases the overall computational complexity. This paper proposes a simple and efficient initialization of Newton’s method to find the regularization parameter. The regularization parameter is chosen to minimize the mean-squared-error (MSE) of a regularized least-squares (RLS) problem by solving a certain function. We exploit some properties of this function to develop the method. Simulation results demonstrate that the proposed initialization method can reduce the runtime by 75%.

This paper deals with the problem of automatic diagonal loading for the linear constrained minimum power beamformer. To find the beamformer’s weights, the linear constrained minimum power problem is reformulated into its generalized sidelobe canceller implementation, which is an unconstrained least-squares problem. To solve this problem, we utilize a bounded perturbation regularization approach where a perturbation matrix with a bounded norm is added to the linear transformation matrix of the least-squares problem in order to enhance the singular-value structure of the matrix. Compared to different diagonal loading methods, the proposed method shows superiority in performance when the number of snapshots is limited.

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.

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 many biological applications, the primary objective of study is to quantify the magnitude of treatment effect between two groups. Cohens'd or strictly standardized mean difference (SSMD) can be used to measure effect size however, it is sensitive to violation of assumption of normality. Here, we propose an alternative metric of standardized effect size measure to improve robustness and interpretability, based on the overlap between two sample distributions. The proposed method is a non-parametric generalized variant of SSMD (Strictly Standardized Mean Difference). We characterized proposed measure in various simulation settings to illustrate its behavior. We also investigated finite sample properties on the estimation of effect size and draw some guidelines. As a case study, we applied our measure for hit selection problem in an RNAi experiment and showed superiority of proposed method., Accepted in International Conference on Bioinformatics and Biomedicine (BIBM) 2020. arXiv admin note: text overlap with arXiv:2001.06384

Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

In this article, a graphene-based multilayered surface plasmon resonance (SPR) biosensor of (BK7/WS2/Au/BaTiO3/Graphene) is proposed for the rapid detection of the novel coronavirus (COVID-19). The proposed SPR biosensor is designed based on the angular interrogation attenuated total reflection (ATR) method for rapid detection of the COVID-19 virus. The sensor’s surface plasmon polaritons (SPPs) and the sensing region refractive index (RI) are changed, owing to the interaction of various concentrated ligand-analytes. The specific ligand is mechanized with the proposed sensor surface and the target analyte that has flowed onto the sensing surface. The proposed sensor is capable of detecting the COVID-19 virus rapidly in two different ligand-analytes environments, such as: (i) the virus spike receptor-binding domain (RBD) as an analyte and monoclonal antibodies (mAbs) as a probe ligand, and (ii) the monoclonal antibodies (IgG or IgM) as an analyte and the virus spike RBD as a probe ligand. Due to the binding of the target ligand-analytes, the concentration level of the sensing region is incremented. As the increment in the concentration level, the RI of the sensing medium increases, therefore the change in RI causes the shift in the SPR angle resulting in the output reflectance intensity. The performance of the multilayered SPR sensor is analyzed numerically using the finite element method (FEM) method. Numerically, the proposed sensor provides the maximum angular shift sensitivity at 230.77 deg/refractive index unit (RIU), detection accuracy (DA) at 0.161 deg−1, and the figure of merits (FOM) is at 37.22 RIU−1. In addition, with each additional graphene layer number (L), the proposed sensor exhibits the angular shift sensitivity increment (1 + 0.7L) times. The novelty of the proposed multilayer (BK7/WS2/Au/BaTiO3/Graphene) sensor is highly angular sensitivity, and capable of detecting the COVID-19 virus rapidly without a false-positive report.