Movement Of Unmanned Aerial Vehicles Research Articles

Analytical synthesis of the control laws at short-period longitudinal motion of the unmanned aerial vehicle

Analytical expressions of control laws for a linearized model of short-period longitudinal motion of a third-order unmanned aerial vehicle are obtained. The synthesis is based on the original decomposition of the control object and the method of modal state-based control of a dynamic system, previously developed on its basis. The numerical modeling results of the aircraft short-period longitudinal motion control by using analytically synthesized control laws, which are graphs of transient processes and control actions, are presented. Keywords decomposition, modal synthesis, longitudinal short-period motion of an aircraft, poles of a dynamic system

Hybrid Dual-Scale Neural Network Model for Tracking Complex Maneuvering UAVs

Accurate tracking and predicting unmanned aerial vehicle (UAV) trajectories are essential to ensure mission success, equipment safety, and data accuracy. Maneuverable UAVs exhibit complex and dynamic motion, and conventional tracking algorithms that rely on predefined models perform poorly when unknown parameters are used. To address this issue, this paper introduces a hybrid dual-scale neural network model based on the generalized regression multi-model and cubature information filter (GRMM-CIF) framework. We have established the GRMM-CIF filtering structure to differentiate motion modes and reduce measurement noise. Furthermore, considering trajectory datasets and rates of motion change, a neural network at different scales will be designed. We propose the dual-scale bidirectional long short-term memory (DS-Bi-LSTM) algorithm to address prediction delays in a multi-model context. Additionally, we employ scale sliding windows and threshold-based decision-making to achieve dual-scale trajectory reconstruction, ultimately enhancing tracking accuracy. Simulation results confirm the effectiveness of our approach in handling the uncertainty of UAV motion and achieving precise estimations.

KOGPSR: A 3D GPSR algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna

Nowadays, flying ad hoc network (FANET) has captured great attention for its huge potential in military and civilian applications. However, the high-speed movement of unmanned aerial vehicles (UAVs) in three-dimensional (3D) space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms. To improve the performance of packet transmission in the 3D high dynamic FANETs, we propose a 3D greedy perimeter stateless routing (GPSR) algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna (KOGPSR). Especially, in data forwarding part of the KOGPSR, we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs, and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing. In addition, in order to enhance the accuracy of the location information of high dynamic UAVs, we design an adaptive Kalman algorithm to track and predict the motion of UAVs. Finally, a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm. The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.

Enhanced stability of aerial refueling process of UAV based on fuzzy PID

This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.

Rotating 3D laser mapping system for multi-rotor drones

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

High-Performance Detection-Based Tracker for Multiple Object Tracking in UAVs

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.

A real‐time fuzzy motion planning system for unmanned aerial vehicles in dynamic 3D environments

This paper presents a new fuzzy potential system to plan the motion of Unmanned Aerial Vehicles (UAVs) in dynamic 3D Space. The system consists of two fuzzy subsystems representing the attractive model and the repulsive model of virtual forces in 3D. The attractive model will generate the attractive force required to pull the UAV in a smooth and optimized trajectory to land softly on a moving or stationary target. The repulsive model will generate the required repulsive force to avoid stationary or moving obstacles in 3D Space. The attractive fuzzy inference system takes the relative position and relative velocity between UAV and the target in the x, y, and z directions as inputs. It generates the required attractive force in the x, y, and z directions. The repulsive fuzzy inference system takes the relative position between UAV and obstacle in the xyz directions as input. Fuzzy associative memory (FAM) models the inputs and generates the required repulsive force in the x, y, and z directions. As a result, the UAV is considered to be moving under the influence of fuzzy virtual attractive and repulsive forces simultaneously. Accordingly, it will be able to change both its altitude and projected planner position concurrently and resolves the local minima problem if occurred. On the other hand, many classical models in dynamic environments require several additional inputs, such as the relative position and relative velocity, which increase the requirement on the measurement system to localize the moving objects in the 3D Space. Several experiments were performed and discussed to verify the robustness and effectiveness of the proposed motion planner with real-time implementations. The system performance was validated using three robotics platforms, two quadcopter drones, and one ground robot. The position and orientation of each robot were defined using a motion capture system with 6 opti-track cameras. The motion planning system produces a quadcopter drone's efficient and accurate low-frequency trajectory. The generated trajectory allows the drone to track the ground robot and avoid collision effectively with the second drone in the vicinity.

Trajectory Planning and Control Design for Aerial Autonomous Recovery of a Quadrotor

One of the most essential approaches to expanding the capabilities of autonomous systems is through collaborative operation. A separated lift and thrust vertical takeoff and landing mother unmanned aerial vehicle (UAV) and a quadrotor child UAV are used in this study for an autonomous recovery mission in an aerial child–mother unmanned system. We investigate the model predictive control (MPC) trajectory generator and the nonlinear trajectory tracking controller to solve the landing trajectory planning and high-speed trajectory tracking control problems of the child UAV in autonomous recovery missions. On this basis, the estimation of the mother UAV movement state is introduced and the autonomous recovery control framework is formed. The suggested control system framework in this research is validated using software-in-the-loop simulation. The simulation results show that the framework can not only direct the child UAV to complete the autonomous recovery while the mother UAV is hovering but also keep the child UAV tracking the recovery platform at a speed of at least 11 m/s while also guiding the child UAV to a safe landing.

МОДЕЛИРОВАНИЕ ДВИЖЕНИЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ

The relevance is due to insufficient knowledge and development of movement patterns for unmanned aerial vehicles (UAVs), which include all possible drones and, in particular, various copters in mobile and turbulent atmosphere. Unmanned aerial vehicles are used both in civilian industries and in military affairs [1 – 4]. When operating UAVs in real conditions, their movement is affected by disturbances from air masses [5, 6], which are generally random in nature. At the same time, the operator is faced with inadequate UAV behaviour caused by disturbances, which makes it difficult to control. The aim of the article is to simulate the complex movement of UAVs, mainly quadrocopters, in space to understand the dynamics of UAV flight under the influence of several forces. The models are based on kinematics and dynamics of a material point, in the form of which the UAV is represented, involved in complex relative and translational movements. Kinematic models of drone movement patterns based on coordinate transformation are de-scribed. Planar and spatial trajectories of UAV movement are presented.

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space.

MULTI-AGENT MODEL OF GROUP CONTROL POINT OF UNMANNED AIRCRAFT AND UNMANNED GROUND VEHICLE WHEN PERFORMING SPECIAL MISSIONS

Unmanned aerial vehicles (UAVs) are becoming very important platforms for conducting combat operations in the russian-ukrainian war, as they significantly reduce the risk of human life during special and combat missions. The UAV group can be used to perform a wide range of military tasks: destruction of enemy equipment, reconnaissance (monitoring) of the area, construction of aerial networks, etc.
 In the conditions of a change in the tactical situation, countermeasures by the enemy, effective management of the UAV group is impossible without an appropriate management system. The UAV group can include both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UAVs). The following main tasks are assigned to the management system: determination and coordination of the order of joint actions of autonomous UAV groups; formation of flight (ground) tasks and flight (movement) trajectories of the UAV group; dynamic adjustment of the flight task and the role of each UAV during its use; organization of the communication network between the control center and between UAV groups, etc. The tasks and target functions of management of each of their subsystems are determined. The features of the UAV group control cycle are described.
 The main stages of the synthesis of the control system are considered: the choice of the control method (centralized, hierarchical, decentralized and mixed); selection of algorithms (protocols) and organization of the process of data exchange between UAVs. A functional model of the UAV group management system is proposed. Tasks and main management functions of each of the subsystems are defined. Features of the UAV group management cycle are described. The main stages of the synthesis of the control system are considered: the selection of algorithms (protocols) and the organized process of information exchange between UAVs; variants of formation and swarm models of UAV movement in groups; multi-agent decision-making models for the organization of task performance by the UAV group.

Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm

The study of unmanned systems has garnered significant academic attention worldwide. The advancements in tracking technologies, particularly the unmanned aerial vehicle (UAV) developed by the US Air Force, have motivated scientists to extensively explore this field due to the abundance of available aerial resources. However, the UAV’s effectiveness in motion is influenced by its three fundamental motions: pitch, roll, and yaw. To address this, scientists have been developing optimization algorithms and models to enhance the UAV’s performance. Nonetheless, in practical scenarios, each motion requires specific measurements, and their relative importance varies. This presents several challenges, including the existence of multiple criteria for selecting optimization values, determining the criteria’s significance, and evaluating trade-offs between performance across different optimization cases. Consequently, the selection and evaluation of UAV motion control algorithms becomes complex. This study proposes a novel selection process that employs the cuckoo optimization algorithm (COA) and real measured resizable margins for goal detection instead of traditional fixed-size evaluation criteria. The framework consists of enhancing PID motion gains using the COA algorithm, focusing on altitude and attitude functional PID controllers. The results of the selection process demonstrate the significance of various criteria for different motions. Rigorous evaluation and analysis were conducted to validate the proposed research framework.

Multi-Agent Bifurcation Consensus-Based Multi-Layer UAVs Formation Keeping Control and Its Visual Simulation

This paper focuses on the mult-layer unmanned aerial vehicles (UAVs) formation keeping control issue based on multi-agent bifurcation consensus. Taking the two-layer formation as an example, due to delays in receiving and responding to signals, the intra-layer and inter-layer control schemes under the dynamic properties need to be considered. Firstly, the UAV heading autopilot is designed to fully consider the control of the angle of attack and yaw angle, which is utilized to deal with the translational and angular motion of UAV. Based on the heading autopilot, the stability and Hopf bifurcation conditions of UAVs heading dynamic system are discussed to get real-time state information and generate control input. Secondly, the leader-following formation combined with the virtual control structure to obtain a virtual hierarchical control strategy, and then a bifurcation consensus criterion. The results shown that when UAVs heading control system achieves bifurcation consensus. The formation maintains the shape while showing interesting periodic oscillation motion behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the presented control strategy. Particularly, visualization simulation using 3ds Max software makes the results more intuitive and flexible.

Multi-UAV computing enabling efficient clustering-based IoT for energy reduction and data transmission

Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises.

Increasing the accuracy of finding the coordinates of sea mines by means of kinematic design

The issue of demining reservoirs, rivers and sea raids and ports, which is relevant for Europe during the period of active deployment of military confrontations on its territory, is considered. Only in the maritime territorial waters of Ukraine and in the water areas of its freshwater reservoirs and rivers, during the year of the war, the enemy laid up to a thousand different mines. Demining of the Ukrainian water spaces of the sea, reservoirs and rivers is an important social and economic problem, the solution of which does not tolerate delay and waiting for the full end of hostilities.
 The purpose of the work is the development of principle schemes for optimizing the spatial movements of search and mine clearance vessels and the creation of algorithms for calculating the precise coordinates of the spatial location of floating, anchor and bottom mines by means of kinematic design.
 The research methodology was the application of the theory of mapping coordinates and trajectories of spatial movements of moving objects by means of graphic geometry, taking into account the specifics and dynamic features of kinematic design.
 A schematic diagram of the spatial location of search trawlers and UAVs or auxiliary search floats when searching for mines has been developed. Mathematical dependencies have also been established for the precise calculation of the coordinates of detected mines based on the data of the applied theory and kinematic design schemes. For the search for floating mines, the optimal trajectory of search movements of unmanned aerial vehicles along the Archimedean spiral is proposed. The optimal number of aircraft that search for floating mines at the same time has been determined. As one of the most effective options for the disposal of mines, their detonation with warheads dropped from an aerial liquidator drone has been proposed.
 The main result of the study is the creation of a methodology for calculating the refined coordinates of the search for floating, anchor and bottom mines in combination with the optimization of the trajectories of the search movements of aerial and floating search vehicles along the Archimedean spiral. This ensures not only a 25-30% increase in surveyed and demined areas, but also a proportional saving of fuel due to a reduction in the number of crossings of search trawlers. It has been established that at speeds of spatial movements of search aircraft or auxiliary floating means of 5÷5.5 m/s, using the proposed search scheme, it is possible to survey up to 6 square kilometers of sea water area every hour. At the same time, it is possible to save up to a third of the cost of expensive fuel due to the reduction of movements of the trawler boat.

An Efficient Evolution-Based Technique for Moving Target Search with Unmanned Aircraft Vehicle: Analysis and Validation

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics.

A Bionic Dynamic Path Planning Algorithm of the Micro UAV Based on the Fusion of Deep Neural Network Optimization/Filtering and Hawk-Eye Vision

A micro unmanned aerial vehicle (UAV) only equipped with a monocular camera is hard to accomplish a flying task with obstacles avoidance and target tracking simultaneously. In this article, a bionic dynamic path planning algorithm was developed for cooperation of obstacles avoidance and target tracking. An improved bat algorithm (BA) optimized transfer learning convolutional neural network (CNN) and bio-inspired optical flow balance algorithm was combined for obstacles avoidance. The Hawk-eye algorithm with line of sight (LOS) tracking rules is aimed at UAV dynamic tracking with obstacles avoidance. All of perception information, including avoidance and tracking were fused in UAV motion decision phase. The experiments include “obstacles avoidance” and “obstacles avoidance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> target tracking” parts. Comparing with manual control and other algorithms, the bionic dynamic path planning algorithm in this article showed certain advantages in success rate, less obstacles collisions, and less major accidents.

КЛАССИФИКАТОР БПЛА МУЛЬТИРОТОРНОГО ТИПА

This article discusses a classifier of radar signals reflected from unmanned aerial vehicles(UAVs), based on neural networks. In the proposed classifier, for the formation of training data, a modelof scattering of radar signals from UAVs is used. Recently, the demand for UAV classification hasbeen quite high due to a significant increase in the number of models and sales of these devices. Increasingthe computing power of processors and the development of the theory of neural networks allows youto create new types of classifiers. When using models, it is possible to create a set of training data that isacceptable for training a classifier neural network. The convolutional neural network of the classifier istrained using radar images obtained using the proposed model of scattering of radar signals fromUAVs. The resulting radar images are modeled taking into account the UAV orientation angles relativeto the UAV normal coordinate system, flight speed, and various propeller parameters of the simulatedUAV. To form training data, in addition to the signal structure, white noise of a certain configuration isadded, which helps to increase the diversity of training samples to improve the learning ability of theconvolutional neural network. The use of data obtained using the model for training a neural network isdue to the need to use a large number of training samples with various UAV movement parameters,such as height, speed, direction, orientation in space, as well as a wide variety of possible configurationsof unmanned aerial vehicles: tricopter (three propellers), quadcopter (four propellers), hexacopter(six propellers), or octocopter (eight propellers). which complicates the use of experimental data tocreate classifiers of this type.

Collaborative WSN-UAV Data Collection in Smart Agriculture: A Bi-objective Optimization Scheme

Smart agriculture which integrates the agriculture with Internet of Things (IoT) has attracted attention since it contributes to increase the productivity and quality of crops, reduce energy consumption and facilitate the farmers. Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are two most commonly deployed devices that are used for enabling the smart agriculture. In this paper, we design a collaborative WSN-UAV system, wherein different clusters of sensor nodes form different sensor-based virtual antenna arrays (SVAAs) to transmit the collected data towards different receiver UAVs via adopting collaborative beamforming (CB), then the receiver UAVs will take the collected data back to the ground control station (GCS). We formulate a transmission rate and battery energy bi-objective optimization problem (TRBEBOP) to simultaneously maximize the total transmission rate of the sensor-based CB clusters and the total remaining battery energy of the selected sensor nodes, by selecting appropriate sensor nodes in each cluster that can form a predominant SVAA, determining suitable receiver UAVs and optimizing the excitation current weights of the selected sensor nodes. To handle the formulated TRBEBOP that is demonstrated to be non-convex and NP-hard, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) with several specific designs is presented. Simulation results validate the effectiveness of the proposed ENSGA-II for solving the formulated TRBEBOP. Moreover, compared with other benchmark algorithms, the superiority of the proposed ENSGA-II is demonstrated. In addition, the impacts of several fortuitous circumstances on the system are estimated, and the results illustrate the robustness of the proposed scheme. Finally, the discussion about several mechanisms to deal with the interference induced by the sidelobe levels and the impact of UAV movement on receiving rate are provided.

Adaptive observer‐based attack estimation and event‐triggered security control for UAVs

SummaryWith the increasingly close connection between unmanned aerial vehicles (UAV) and communication networks, it is facing an increasing number of cybersecurity threats. In this article, an event‐triggered security control problem for UAVs is investigated under the tampered input signal sent by the ground control station. First, a linear model of the lateral movement of UAV is established and the attack description is given. Second, an adaptive observer is developed to reconstruct the attack signal. The reconstructed signal is compensated as system disturbance, and then the safety control strategy with event‐triggered mechanism (ETM) is designed. Furthermore, through the time interval analysis approach, the ETM, state observer, controller and attack signal are unified into a new augment system. Then, the uniformly ultimately bounded analysis and controller/observer synthesis conditions are obtained by using piecewise Lyapunov–Krasovskii functional methods. Furthermore, a sufficient condition for the parameters involving controller gain, observer gain, and the ETM parameters is given via a linear matrix inequality. Finally, simulation results are conducted to verify the effectiveness of the proposed method.