Showing papers by "Ahmad Taher Azar published in 2020"

Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments.

Abstract: Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called modified aging ant colony optimization (AACO). The AACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

New binary whale optimization algorithm for discrete optimization problems

Abstract: The whale optimization algorithm (WOA) is an intelligence-based technique that simulates the hunting behaviour of humpback whales in nature. In this article, an adaptation of the original version o...

A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization

Abstract: The design of a swarm optimization-based fractional control for engineering application is an active research topic in the optimization analysis. This work offers the analysis, design, and simulation of a new neural network- (NN) based nonlinear fractional control structure. With suitable arrangements of the hidden layer neurons using nonlinear and linear activation functions in the hidden and output layers, respectively, and with appropriate connection weights between different hidden layer neurons, a new class of nonlinear neural fractional-order proportional integral derivative (NNFOPID) controller is proposed and designed. It is obtained by approximating the fractional derivative and integral actions of the FOPID controller and applied to the motion control of nonholonomic differential drive mobile robot (DDMR). The proposed NNFOPID controller’s parameters consist of derivative, integral, and proportional gains in addition to fractional integral and fractional derivative orders. The tuning of these parameters makes the design of such a controller much more difficult than the classical PID one. To tackle this problem, a new swarm optimization algorithm, namely, MAPSO-EFFO algorithm, has been proposed by hybridization of the modified adaptive particle swarm optimization (MAPSO) and the enhanced fruit fly optimization (EFFO) to tune the parameters of the NNFOPID controller. Firstly, we developed a modified adaptive particle swarm optimization (MAPSO) algorithm by adding an initial run phase with a massive number of particles. Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase. The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079. The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories. The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases.

Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

Abstract: The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are ...

Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System

Abstract: A consensus control law is proposed for a multi-agent system of quadrotors with leader-follower communication topology for three quadrotor agents The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller Different case studies and tests are done (ie, trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller The results show good performance in all tests while achieving the consensus of the desired formations

On Inverse Full State Hybrid Function Projective Synchronization For Continuous-time Chaotic Dynamical Systems with Arbitrary Dimensions

Abstract: Referring to continuous-time chaotic dynamical systems, this paper investigates the inverse full state hybrid function projective synchronization (IFSHFPS) of non-identical systems characterized by different dimensions. By taking a master system of dimension n and a slave system of dimension m, the method enables each master system state to be synchronized with a linear combination of slave system states, where the scaling factor of the linear combination can be any arbitrary differentiable function. The approach, based on the Lyapunov stability theory and stability of linear continuous-time systems, presents some useful features: (i) it enables non-identical chaotic systems with different dimension $$nm$$ to be synchronized; (ii) it can be applied to a wide class of chaotic (hyperchaotic) systems for any differentiable scaling function; (iii) it is rigorous, being based on two theorems, one for the case $$nm$$ . Two different numerical examples are reported. The examples clearly highlight the capability of the conceived approach in effectively achieving synchronized dynamics for any differentiable scaling function.

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles.

Abstract: This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

A fractional map with hidden attractors: chaos and control

Abstract: This paper studies the dynamics of a new fractional-order map with no fixed points. Through phase plots, bifurcation diagrams, largest Lyapunov exponent, it is shown that the proposed fractional map exhibit chaotic and periodic behavior. New Hidden chaotic attractors are observed, and transient state is found to exist. Complexity of the new map is also analyzed by employing approximate entropy. Results, show that the fractional map without fixed point have high complexity for certain fractional order. In addition, a control scheme is introduced. The controllers stabilize the states of the fractional map and ensure their convergence to zero asymptotically. Numerical results are used to verify the findings.

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Abstract: Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Abstract: Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

Stabilization of Port Hamiltonian Chaotic Systems with Hidden Attractors by Adaptive Terminal Sliding Mode Control.

Abstract: In this study, the design of an adaptive terminal sliding mode controller for the stabilization of port Hamiltonian chaotic systems with hidden attractors is proposed. This study begins with the design methodology of a chaotic oscillator with a hidden attractor implementing the topological framework for its respective design. With this technique it is possible to design a 2-D chaotic oscillator, which is then converted into port-Hamiltonia to track and analyze these models for the stabilization of the hidden chaotic attractors created by this analysis. Adaptive terminal sliding mode controllers (ATSMC) are built when a Hamiltonian system has a chaotic behavior and a hidden attractor is detected. A Lyapunov approach is used to formulate the adaptive device controller by creating a control law and the adaptive law, which are used online to make the system states stable while at the same time suppressing its chaotic behavior. The empirical tests obtaining the discussion and conclusions of this thesis should verify the theoretical findings.

A Hybrid Deep Learning Based Autonomous Vehicle Navigation and Obstacles Avoidance

Abstract: Technological revolution has reached all life activities starting from day planning reaching communication, entertainment, industry, and transportation. Each of previously mentioned categories get improved in a way making human life easier and safer. In the use of automatic control, several researches focused on automating vehicles’ systems to make driving easier and safer. The availability of autonomous vehicles will avoid accidents caused by taking a late decision or lack of driving experience in such situation. Approaching autonomous driving, an autonomous vehicle must be able to respond to the state of objects in the surrounding, be it stationary or in motion. This paper outlines the techniques which enable the car to become conscious of its immediate environment while it moves independently and to decide its next course of action to avoid obstacles. It investigates two approaches which are Neuro-Fuzzy System tuned by Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN) tuned by Adaptive Moment estimation (Adam). Such control can allow cars on roads to operate smoothly and, according to trained data, take quick accurate decisions. Results showed high performance of deep learning algorithms specially CNN with Adam; however, it needs more computational time than Neuro-Fuzzy system tuned with PSO.

Improved Active Disturbance Rejection-Based Decentralized Control for MIMO Nonlinear Systems: Comparison with The Decoupled Control Scheme

Abstract: A decentralized control scheme is developed in this paper based on an improved active disturbance rejection control (IADRC) for output tracking of square Multi-Input-Multi-Output (MIMO) nonlinear systems and compared with the decoupled control scheme. These nonlinear MIMO systems were subjected to exogenous disturbances and composed of high couplings between subsystems, input couplings, and uncertain elements. In the decentralized control scheme, it was assumed that the input couplings and subsystem couplings were both parts of the generalized disturbance. Moreover, the generalized disturbance included other components, such as exogenous disturbances and system uncertainties, and it was estimated within the context of Active Disturbance rejection Control (ADRC) via a novel nonlinear higher order extended state observer (NHOESO) from the measured output and canceled from the input channel in a real-time fashion. Then, based on the designed NHOESO, a separate feedback control law was developed for each subsystem to achieve accurate output tracking for given reference input. With the proposed decentralized control scheme, the square MIMO nonlinear system was converted into approximately separate linear time invariant Single-Input-Single-Output (SISO) subsystems. Numerical simulations in a MATLAB environment showed the effectiveness of the proposed technique, where it was applied on a hypothetical MIMO nonlinear system with strong couplings and vast uncertainties. The proposed decentralized control scheme reduced the total control signal energy by 20.8% as compared to the decoupled control scheme using Conventional ADRC (CADRC), while the reduction was 27.18% using the IADRC.

Polynomial robust observer implementation based passive synchronization of nonlinear fractional-order systems with structural disturbances

Abstract: A robust polynomial observer is designed based on passive synchronization of a given class of fractional-order Colpitts (FOC) systems with mismatched uncertainties and disturbances. The primary objective of the proposed observer is to minimize the effects of unknown bounded disturbances on the estimation of errors. A more practicable output-feedback passive controller is proposed using an adaptive polynomial state observer. The distributed approach of a continuous frequency of the FOC is considered to analyze the stability of the observer. Then we derive some stringent conditions for the robust passive synchronization using Finsler’s lemma based on the fractional Lyapunov stability theory. It is shown that the proposed method not only guarantees the asymptotic stability of the controller but also allows the derived adaptation law to remove the uncertainties within the nonlinear plant’s dynamics. The entire system using passivity is implemented with details in PSpice to demonstrate the feasibility of the proposed control scheme. The results of this research are illustrated using computer simulations for the control problem of the fractional-order chaotic Colpitts system. The proposed approach depicts an efficient and systematic control procedure for a large class of nonlinear systems with the fractional derivative.

Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning

Abstract: According to its significance, robotics is always an area of interest for research and further development. While robots have varying types, design and sizes, the six degrees of freedom (DOF) serial manipulator is a famous robotic arm that has a vast areas of applications, not only in industrial application, but also in other fields such as medical and exploration applications. Accordingly, control and optimization of such robotic arm is crucial and needed. In this paper, different analyses are done on the chosen design of robotic arm. Forward kinematics are calculated and validated, then simulation using MSC ADAMS is done, followed by experimentation and tracking using Microsoft Kinect. Two approaches are used in this study: adaprive neuro fuzzy (ANF) system optimized by simulated annealing (SA) algorithm and convolutional neural networks (CNNs) optimized by adaptive moment estimation (Adam). The same inputs are given to both models and their results are compared in order to determine the best fit algorithm for higher precision in the given robotic model. The findings have shown that the accuracy of CNNs is higher. Furthermore, this advantage has a higher cost for the time of computation than for NFs with SA.

Classifying Upper Limb Activities Using Deep Neural Networks

Abstract: This paper presents a classification method using Inertial Measurement Unit (IMU) in order to classify six human upper limb activities. The study was also carried out to investigate whether theses activities are being performed normally or abnormally using two different neural networks: Artificial neural network (ANN) and convolutional neural network (CNN). Human activities that were included in the study: arm flexion and extension, arm pronation and supination, shoulder internal and external rotations. Before activities categorization, training data was obtained by the means of an IMU sensor fixed on an armband worn around the forearm. The training data obtained were positions, velocities, accelerations and jerks around x, y and z axes. Training samples of 264 have been collected from 10 participants, 2 women and 8 men from ages 19 to 23. Then, 204 features were extracted from IMU data, nonetheless, 15 features only have been used as inputs to the proposed neural networks because they were the most distinguished ones. After all, the networks classify the data into one of 6 classes and their results were compared. Furthermore, these proposed methods of classification have been validated by real experiments showing that ANN network gives the best performance.

Machine Learning with Blockchain for Secure E-voting System

Abstract: Voting is a central component of a country's political life cycle. Privacy, authentication and integrity of citizens' votes and their data are considered to be essential to any e-voting program. In order to resolve these concerns, we propose a stable e-voting system based on the principles of blockchain and machine learning. We use blockchain to ensure the integrity and security of votes, machine learning model to detect intrusion in voting data centers and e-voting stations. In the proposed model, we use the concepts of personal and public blockchain. The personal blockchain is used for the purposes of voter registration and voting. The public blockchain is used to maintain the integrity of the personal data of the voters by storing the root hash derived from the Merkle hash tree and revealing the results of the voting stations as soon as the voting process is completed. The proposed blockchain-based e-voting system offers transparency, treasury, confidence and prevents intrusion into the information exchange network.

Deep Learning Based Kinematic Modeling of 3-RRR Parallel Manipulator

Abstract: This paper presents a novel low cost design for a 3-RRR Planar Parallel Manipulator (PPM). These manipulators proved their superiority over serial manipulators due to their speed, precision and smaller work space where the work space area is accounted for in the design to ensure that the robot is performing its task in a smooth and simple way without getting into any singularity points. The challenge with PPM is to obtain the kinematic constraint equations of the manipulator due to their complex non-linear behavior. Screw theory is a new approach that is used to compute the direct and inverse kinematics based on the relation between each link and its’ predecessor. The design is then inserted into ADAMS to study its dynamical behavior and to obtain a data set that would be used in analyzing the system in MATLAB. A Neuro-Fuzzy Inference System (NFIS) model was constructed in order to predict the end-effector position inside the work space and it is tuned with Particle swarm optimization (PSO) and Genetic algorithm (GA).

Neural Computing Enhanced Parameter Estimation for Multi-Input and Multi-Output Total Non-Linear Dynamic Models.

Abstract: In this paper, a gradient descent algorithm is proposed for the parameter estimation of multi-input and multi-output (MIMO) total non-linear dynamic models. Firstly, the MIMO total non-linear model is mapped to a non-completely connected feedforward neural network, that is, the parameters of the total non-linear model are mapped to the connection weights of the neural network. Then, based on the minimization of network error, a weight-updating algorithm, that is, an estimation algorithm of model parameters, is proposed with the convergence conditions of a non-completely connected feedforward network. In further determining the variables of the model set, a method of model structure detection is proposed for selecting a group of important items from the whole variable candidate set. In order to verify the usefulness of the parameter identification process, we provide a virtual bench test example for the numerical analysis and user-friendly instructions for potential applications.

Backstepping H-Infinity Control of Unmanned Aerial Vehicles with Time Varying Disturbances

Abstract: In this paper, a backstepping H-Infinity controller for unmanned aerial vehicles (UAV) with time varying disturbances is proposed. The type of unmanned aerial vehicle analyzed in this paper is a quadrotor type, which is divided into position and attitude dynamics. Time-variable disturbances that may be caused by atmospheric or other forms of environmental conditions are considered, taking into consideration the mathematical conditions that describe the form of disturbance modeled on the position dynamics. The design of the H - Infinity backstepping controller is based on a recursive methodology by applying the respective virtual inputs and considering the conditions for the disturbance rejection control. The attitude and position of the backstepping control inputs are determined by selecting the appropriate Lyapunov functions to satisfy the stability conditions of the closed loop. Two numerical examples are presented to test and validate the theoretical results obtained in this analysis. Two types of reference variables are used to verify the position and attitude of the controller and the system response.

Gray Wolf Optimization of Fractional Order Control of 3-Omni Wheels Mobile Robot: Experimental Study

Abstract: Committing robotics with artificial intelligence becomes mandatory collaboration with distinct environments. Omnidirectional Wheeled (Omni-WD) mobile robots are one of the robots that interact with humans in various circumstances, where it is important to function effectively and accurately. In this paper, the distinction of a 3WD-Omni model and control using machine vision is demonstrated. The use of fractional order (FO) calculus has been stated to increase the degrees of freedom of the controller over the integer ones. Hybridization of FO control and metaheuristics optimization is reported to enhance the control performance. Particle Swarm Optimizer (PSO) and Gray Wolf Optimizer (GWO) have been used for tuning the classical PID and FO-PID controllers. A comparative study between the optimized classical PID and FOPID has been performed using different paths and optimization methods. The results of the comparative study are mentored using different performance indices. The superior performance of FOPID tuned by GWO has been proven over all other techniques.

Path Planning of a Self Driving Vehicle Using Artificial Intelligence Techniques and Machine Vision

Abstract: This paper aims to implement an efficient model of the most optimum path to follow an object on a Self Driving Vehicle (SDV). The path of the vehicle is predicted by using Machine Vision (MV) and Neural networks (NN) model. The NN model uses numerous amounts of training data. First the system works by using the MV algorithms to detect objects with predefined colors. Then, the location of the object is fed to the trained NN to get the speeds of the motors needed to reach the object. The training data are obtained from the manual driving of the vehicle in different experiment settings. In this paper, the neural model is compared with two other methods: object detection using MV model and fuzzy logic (FL) model to prove the efficiency of the neural model. All the three models depend on the live record of the camera board and its fast detection of objects using MV algorithms. The three models showed quite similar results; however, the NN model was much more stable and closer to the optimum path.

Stability analysis and robust synchronisation of fractional-order modified Colpitts oscillators

Abstract: Based on the stability theory of the fractional order system, the dynamic behaviours of the uncertain Colpitts oscillator with fractional order-derivative is studied. Furthermore, based on the extended bounded real lemma, the robust controller is obtained using the drive-response synchronisation concept together with the Lyapunov stability theory formulated using the fractional Lyapunov direct method where the fractional-order q belongs to 0 < q < 1. In order to bring out the dynamic behaviour of this system, their phase portraits, the bifurcation diagrams and the Lyapunov exponent are simulated. Moreover, in this work, an approximated solution for both systems to show that the solution of such a system can be represented as a simple power-series function is provided. This study equally provides a systematic procedure to highlight the simplicity and flexibility of the suggested control approach. Simulations with both parameters uncertainty and external disturbance show the applicability and the efficiency of the proposed scheme.

Robust Kinematic Control of Unmanned Aerial Vehicles with Non-holonomic Constraints

Abstract: This paper presents a robust kinematic control of unmanned UAV aerial vehicles with non-holonomic constraints. The studied system consists of a 2D UAV non-holonomic kinematic model represented as a driftless system with its state and control inputs. The first part of this study consists of the evidence that the model being studied is non-holonomic in view of its involutivity properties. The second part of this study consists of the design of a robust kinematic controller for an unmanned aerial vehicle (UAV) in which the states of the systems are used for feedback control and the desired angular and linear velocities are precisely tracked by the proposed controller approach. This control strategy is achieved by designing the appropriate Lyapunov functional to meet the robust stability conditions and by finding the switching gains to track the desired profile. The control strategy obtained is tested in the proposed 2D mathematical model of the unmanned aerial vehicle and it is confirmed that the system variables track the desired profile while keeping the angular and linear velocity bounded. This study concludes with a discussion of the results and the respective conclusions.

Fractional-Order Control of a Fuel Cell–Boost Converter System

Abstract: This paper processes the output voltage control system for energy source based on a fuel cell linked to a DC voltage generator. The suggested technique is based on the design of a fractional controller to regulate the output voltage of this DC–DC converter for distinct resistive load levels. Closed-loop system efficiency is assessed using simulation results. A regulation of the suitable output voltage and a solid conduct regarding the load variation and the DC output voltage are provided.

Adaptive Fuzzy Type-2 Fractional Order Proportional Integral Derivative Sliding Mode Controller for Trajectory Tracking of Robotic Manipulators

Abstract: With the increase of novel tasks for different kinds of robotic manipulators in industrial applications a precise trajectory tracking is needed. For this reason, in this study the design of an adaptive fuzzy type-2 fractional order proportional-integral-derivative (PID) sliding mode controller is designed in order to provide a fast and accurate response for trajectory tracking of robotic manipulators in a reduced task space. Fuzzy type-2 and fractional order sliding mode controllers provide a suitable control strategy when there are uncertainties considering the flexibility of fractional order controller and combined with the efficacy of PID controllers. To design the adaptive laws, a suitable Lyapunov function is selected in order to obtain the global asymptotic stability despite of the initial conditions for the angular positions and velocities of the actuators. It is important to mention that the results will be compared with other results found in literature by using a six degrees of freedom PUMA 560 robot as benchmark. In order to test the proposed strategy a faster response, accuracy and chattering suppression are obtained in comparison with other control techniques.

Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems

Abstract: In this paper, a novel control strategy is shown for the stabilisation of dynamic systems in the form of port-Hamiltonian systems. This hybrid approach composed by a neural fuzzy and backstepping c...

Leader-Follower Control of Unmanned Aerial Vehicles with State Dependent Switching

Abstract: This paper proposes a leader-follower controller for unmanned aerial vehicles. This strategy consists of implementing state-dependent switching laws based on the stability of the closed loop during each switching mode. Multiple unmanned aerial vehicles UAVs are considered to be training around the leader in order to achieve the desired trajectory profile where, in this case, the trajectory of the UAVs is driven to follow the leader along the desired path. State-dependent switching provides advantages compared to other approaches where accurate control action is needed when a change in the leader’s state trajectory occurs, so that the appropriate UAV path command is received to drive the UAV in the desired direction. The stability theorem of Lyapunov is implemented taking into account the use of Metzler matrices to achieve the stability of the closed loop system in different switching modes. The results obtained in this study are validated by two numerical examples under different conditions for observing and testing the theoretical results provided in this paper and for demonstrating the mathematical contributions made in this study.

Nonlinear Fractional Order System Synchronization via Combination-Combination Multi-switching

Abstract: This manuscript focuses on the synchronization of the nonlinear fractional order system by multi-switching combination. Two master systems and two slave systems are considered for combination-combination multiple-switch synchronization technology and various master systems are syncing with different slave systems. The Lorenz method of the nonlinear fractional order is to apply it. The stability of the dynamic structure of fractional order error is studied by pole placement. In order to show that the method is accurate and functional, theoretical results and numerical simulations are given.

Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

Abstract: Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework.