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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.

In this letter, we propose techniques for joint transmit and receive beamforming in downlink multi-user MIMO systems under Rayleigh fading channels using knowledge of the second-order statistics. The contribution of our work is twofold. First, we derive a closed-form expression for the outage probability for a general channel model using a recent covariance shaping method and for the conventional Kronecker structured model. Second, we present an outage minimization technique obtained by designing the transmit and receive beamformers. The transmit beamformers maximize the statistics of the signal-to-leakage-plus-noise ratio, while the receive beamformers minimize the cross-covariance of all users. Our techniques are “blind” in the sense that they do not require the transmission of pilot symbols, nor the estimation of the instantaneous state of the channel, making them bandwidth efficient. The derived theoretical results are validated via Monte Carlo simulations and show significant reduction in the outage probability by employing the proposed statistical beamforming techniques.

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.

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.

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.

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.

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.

This paper addresses the distributed average tracking (DAT) problem for a group of agents to track the average of multiple time-varying reference signals, each of which is available to only one agent, under local interaction with neighbors and an undirected graph. We consider three cases: 1) DAT for single-integrator dynamics with a chain of integrators in algorithm design; 2) DAT with swarm behavior for single-integrator dynamics; and 3) DAT with swarm behavior for double-integrator dynamics. First, a continuous distributed algorithm with a chain of two integrators is proposed for single-integrator dynamics, where each agent needs its own and its neighbors’ filter outputs obtained through communication besides its absolute position, local relative positions, reference signal, and reference velocity. The introduced algorithm can deal with a wide class of reference signals with steady deviations among reference velocities. Then, two swarm-based DAT algorithms for, respectively, single- and double-integrator dynamics are presented. In the first swarm-based algorithm for single-integrator dynamics, each agent needs to measure the relative positions between itself and its neighbors, while in the second swarm-based algorithm for double-integrator dynamics, relative velocity information is required too. In both cases, the information can be obtained through local sensing. If correct initialization constraints can be achieved, the center of the agents will track the average of the reference signals and the agents will maintain connectivity and avoid interagent collision. Numerical simulations are also presented to illustrate the theoretical results. Finally, the algorithms presented in this paper are experimentally implemented and validated on a multirobot platform.

This paper focuses on distributed estimation using networked sensor agents with local measurements and local communication. A distributed Kalman–Bucy filter implementation is proposed and analyzed for the general case of continuous-time linear time-varying systems as compared to linear time-invariant systems. The sensor agents employ a distributed average tracking algorithm and its extension that accounts for bounded noise to estimate the averages of certain time-varying signals by communicating with their local neighbors in the network. Such estimates are then used to recover certain information in the implementation of the distributed Kalman–Bucy filter. It is shown that using the proposed distributed filter, in the absence of measurement noise, the distributed local estimates approach the centralized filter's estimate asymptotically. In the presence of bounded measurement noise, the distributed local estimates asymptotically approach the centralized filter's estimate within some bound. Simulation results illustrate the good performance of the distributed filter.

This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has communication, sensing, moving, and computation capabilities to complete sensing tasks. These agents would have some limitations on time and energy to accomplish their tasks that need to be considered by the designers. Here, the agents need to relocate themselves, from their initial random locations, to their optimal configuration. An algorithm based on game theory is proposed, where a collection of distributed agents communicate with local neighbors and use their local information make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade-off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Gaussian Mixture Model (GMM) is used to model the distributions of the worth in the sensing area. To estimate the unknown parameters of the GMM, a Maximum Likelihood (ML) estimation scheme is employed, where an expectation-maximization algorithm is used as a tool to solve the ML recursively. Then, in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents’ utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Both simulation results and experimental results on a multi-robot platform are provided to validate the performance of the proposed algorithm.

This communication attempts to characterize the performance metrics of downlink Massive MU-MIMO systems impaired by cochannel interference and additive noise over a Rayleigh fading environment. We obtain close-form solutions for the probability density function of signal-to-interference-plus-noise ratio (SINR) and the sum ergodic capacity. The proposed work structures SINR in a quadratic form and thereby imposes a condition on its signal and interference power for a large transmit antenna diversity order; the conditional form is then analyzed using a distance correlation metric. Eventually, the sum ergodic capacity is expressed in a close-form by means of a residue theory approach and validated using the Monte Carlo simulation means.

Desire of inexpensive Electro-Optic and Micro-Electro-Mechanical System (MEMS) inertial sensors has drastically been increased in the recent times for both military and commercial applications. Beside the traditional applications, reduced cost of such sensors has opened new domains in personal navigation. This paper provides a framework for attitude estimation using miniaturized and cost-effective Inertial Measurement Units (IMU). Sensor fusion of gyroscope, accelerometer and True Air Speed (TAS) sensor helps in minimizing the characteristic time growing error present in gyroscopic integrated data. A novel approach of TAS model development is implemented to generate true air speed data in the absence of TAS sensor. The presence of linear acceleration is estimated and eliminated by means of gyroscope and TAS model. Due to the slight difference in two direction vectors, an error function is estimated and constantly compensated by using a Proportional-Integral (PI) block. Coarse tuning of PI gains is applied and simulated results are produced to assess the filter performance by using real vehicle data. It has been presented that the proposed filter can be used to compute a reasonably accurate attitude solution by using low cost inertial sensors when external aiding is unavailable or not useful.

Here, the authors develop a method of simultaneous optimisation of precoder and equaliser for the multi-user multiple-input multiple-output (MIMO) system by minimising outage probability. In order to do that, the authors first characterise the signal-to-interference-plus-noise ratio of downlink multi-user MIMO system in the presence of both multi-user interference and self-interference. The closed-form expression for the outage probability is derived in terms of the eigenvalues of an indefinite weight matrix of a quadratic norm. Next, the authors develop an active-set algorithm-based precoder and equaliser by minimising the derived outage probability expression. This work investigates the effect of various system parameters such as the number of users, the number of multi-path channels, the number of transmitting and receiving antenna elements, and the additive noise variance on the outage probability and bit error rate. Simulation results validate the theoretical analysis for multiple system configurations. Lastly, the authors show that the designed precoder and equaliser improves the overall system performance.

In this study, a high gain observer with delayed driving control signal is designed for synchronisation of fractional-order chaotic systems. The method consists in using two cascade high gain observers in order to compensate for the delay transmission signal from the transmitter to the receiver. For large delay, a fractional-order chain observer composed by high gain cascade delayed observers is proposed. The last observer of this chain estimates the state at the current time while previous observers estimate delayed states. Convergence of the proposed observer is proven. Application to the synchronisation of fractional-order chaotic systems is given. Computer simulations show the effectiveness of the proposed approach.

The Least Mean Square (LMS) algorithm inherits slow convergence due to its dependency on the eigenvalue spread of the input correlation matrix. In this work, we resolve this problem by developing a novel variant of the LMS algorithms based on the q-derivative concept. The q-gradient is an extension of the classical gradient vector based on the concept of Jackson?s derivative. Here, we propose to minimize the LMS cost function by employing the concept of q-derivative instead of the convent ional derivative. Thanks to the fact that the q-derivative takes larger steps in the search direction as it evaluates the secant of the cost function rather than the tangent (as in the case of a conventional derivative), we show that the q-derivative gives faster convergence for q 1 when compared to the conventional derivative. Then, we present a thorough investigation of the convergence behavior of the proposed q-LMS algorithm and carry out different analyses to assess its performance. Consequently, new explicit closed-form expressions for the mean-square-error (MSE) behavior are derived. Simulation results are presented to corroborate our theoretical findings. HighlightsDevelopment of the q-gradient.Development of the q-LMS algorithm.Derivation of closed-form expressions for the mean-square-error for the proposed algorithm.Extensive simulation results are carried out to corroborate the theoretical findings.

This paper deals with pole placement PI-state feedback controller design to control an integer order system. The fractional aspect of the control law is introduced by a dynamic state feedback as u(t)=K(p)x(t)+K(I)I(α)(x(t)). The closed loop characteristic polynomial is thus fractional for which the roots are complex to calculate. The proposed method allows us to decompose this polynomial into a first order fractional polynomial and an integer order polynomial of order n-1 (n being the order of the integer system). This new stabilization control algorithm is applied for an inverted pendulum-cart test-bed, and the effectiveness and robustness of the proposed control are examined by experiments.

Analytical methods are usually applied for tuning fractional controllers. The present paper proposes an empirical method for tuning a new type of fractional controller known as PID-Fractional-Order-Filter (FOF-PID). Indeed, the setpoint overshoot method, initially introduced by Shamsuzzoha and Skogestad, has been adapted for tuning FOF-PID controller. Based on simulations for a range of first order with time delay processes, correlations have been derived to obtain PID-FOF controller parameters similar to those obtained by the Internal Model Control (IMC) tuning rule. The setpoint overshoot method requires only one closed-loop step response experiment using a proportional controller (P-controller). To highlight the potential of this method, simulation results have been compared with those obtained with the IMC method as well as other pertinent techniques. Various case studies have also been considered. The comparison has revealed that the proposed tuning method performs as good as the IMC. Moreover, it might offer a number of advantages over the IMC tuning rule. For instance, the parameters of the fractional controller are directly obtained from the setpoint closed-loop response data without the need of any model of the plant to be controlled.

A new technique for constructing the dynamic equivalents for power systems is developed. The technique utilizes a balanced state-space representation to determine the order, state matrix, and either the input matrix or the output matrix of the reduced-order model. The other matrix is found such that the reduced-order model matches the full-order model at both high and low frequencies. The suitability of the new technique for obtaining reduced-order models for the dynamic control of a power plant is demonstrated on a single-machine infinite-bus system.

Four relatively recent techniques for model reduction (balanced, weighted balanced, optimal Hankel and weighted optimal Hankel) are reviewed. The usefulness of these techniques for obtaining reduced-order models of power systems for dynamic control purposes, their effectiveness, and the merits of the simplified models are illustrated on a single-machine infinite-bus system. The methods are evaluated on the basis of Lα errors, transient responses, and steady-state errors. The superiority of the frequency-weighting methods is clearly demonstrated.

The relationship between subsystem interconnection of discrete balanced systems and the Hankel singular values is explored. Upper bounds on the spectral norm of the submatrices of the discrete balanced representation are derived by approximating the matrix Riccati equation by its power series expansion with respect to two scalar parameters related to the Hankel singular values. For diagonalizable symmetric discrete-time systems, the relation between the eigenvalues of the subsystems and the eigenvalues of the overall system is explored. It is shown that an upper bound on the distance between corresponding eigenvalues in these two sets is related to the ratio of the Hankel singular values. >

Advanced controller synthesis techniques often result in high order controllers whose order exceeds the order of the plant and it is impractical to implement. The main use of the advanced synthesis techniques is to find the possible optimal performance of the system and thus to check how a practical simpler controller comes close to achieving that optimum. PID controllers are the most found in industry and here we describe a novel technique, based on approximation, to tune the parameters of PID controllers.

In [1], an algorithm is given for the design of digital controllers. The controller parameters were obtained by minimizing an objective function in terms of the controller parameters and a non-linear optimization technique was employed. Here we point out that the proposed objective function is linear in the controller parameters and the minimization problem is a standard linear least squares problem.

A quantitative reliability model for a phased mission system is developed using a Markov process. Two cases for the mission-phase change times are assumed: 1) to be known in advance and 2) to be random variables. A method of solution is presented and illustrated by examples. The solution of phased-mission systems is equivalent to solving a sequence of uni-phase systems with appropriate initial conditions.

Two model-reduction methods for discrete systems related to balanced realizations are described. The first is a technique which utilizes the least controllable and observable subsystem in deriving a balanced discrete reduced-order model. For this technique as L/sup infinity / norm bound on the reduction error is given. The second method is a frequency-weighting technique for discrete- and continuous-time systems where the input-normal or output-normal realizations are modified to include a simple frequency weighting. For this technique, L/sup infinity / norm bounds on the weighted reduction errors are obtained. >

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an Linftybound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.