In this paper, an interval observer-based fault diagnosis mechanism and an interval dynamic event-triggered robust fault-tolerant control strategy are proposed for uncertain nonlinear switched systems. Firstly, the interval observer design problem is transformed into a Sylvester equation solution problem by introducing the coordinate transformation method in the absence of actuator faults, and the corresponding interval observer design method is given. Secondly, based on the interval observer output, upper and lower bound estimates of the system output are constructed, and fault diagnosis is realized by monitoring whether the system output exceeds the preset bounds. In addition, an improved interval dynamic event triggered mechanism is proposed to optimize the utilization of communication and computational resources and reduce redundant information transmission. The mechanism incorporates interval estimation information and real-time residuals, which can effectively extend the triggering time interval while improving the fault detection performance. Based on the interval fault diagnosis results, a fault-tolerant control strategy based on the interval dynamic event triggered mechanism is proposed, and the conditions for asymptotic stabilization of the closed-loop system are given. Finally, a nonlinear model of a morphing aircraft system with variable wing curvature is used to verify the validity of the designed scheme. DOI: 10.61416/ceai.v28i1.9516

Industry 4.0 and digital factories leverage key technologies such as virtual reality (VR) to design, simulate, optimize, and interact with physical production systems remotely or collaboratively. Recent advancements in industrial and aerospace applications demonstrate that the integration of VR with versatile robotic control techniques can unlock new opportunities. VR offers an "extended arm" to physical environments by providing a user-friendly human-machine interface, enhancing sustainability, testability, and more in robotic control systems. This research focuses on a widely used and continuously evolving robotic arm, designed to function in an integrated system operating in both real and virtual environments. A 3-degree-of-freedom (3DOF) robotic arm was initially developed and later upgraded to a 5DOF system. The robotic arm integrated with Virtual Reality (VR) interfaces based on the Robot Operating System (ROS) and Unity3D platforms. In the real-world scenario, inverse kinematics applied for the robot's movement mechanism, while gradient descent algorithms used for the virtual model. Forward kinematics using homogeneous transformation matrices employed to verify the accuracy of the robotic arm's movements. VR headsets and controllers used to manage the virtual robotic arm's control. The goal of this study is to develop integrated systems that enable control of real-world operations via remote observation, addressing challenges like prototyping costs and safety risks. By offering an innovative approach to robotic arm control through VR integration, this work contributes to the advancement of Industry 4.0 technologies. The findings suggest potential applications across various industries, opening up new possibilities for remote operation and training in complex manufacturing environments. DOI: 10.61416/ceai.v28i1.9592

Efficient Maximum Power Point Tracking (MPPT) techniques play a vital role in optimizing energy extraction from photovoltaic (PV) systems, primarily due to the high sensitivity of photovoltaic panel (PVP) characteristics to varying environmental parameters, including ambient temperature and solar irradiance. Conventional MPPT methods generally depend on identifying and following reference points, which are extracted from variables closely related to the Maximum Power Point (MPP) on the external current-voltage characteristics of PVP. In response to the limitations inherent to traditional approaches, this study proposes an advanced lookup table strategy to generate highly accurate reference values explicitly as functions of measured ambient temperature and solar irradiance. Initially, this research employs the two-diodes electrical circuit model to rigorously define the foundational relationships between ambient temperature, irradiance, and photovoltaic panel performance. Subsequently, contemporary advanced extraction methods based on recent academic literature were applied, enabling accurate identification of the MPP coordinates. Creating an effective lookup table requires comprehensive and detailed experimental datasets, which are often insufficient or unevenly distributed in practical scenarios. To address this challenge, the Shepard global interpolation method was integrated into the model development process, effectively mitigating datasets limitations through accurate interpolation of missing data points. This approach significantly elevates the precision and robustness of MPPT models. A comprehensive case study, conducted with real-world experimental datasets, rigorously validated the accuracy and reliability of the proposed interpolation-based lookup table. The results confirm substantial enhancements in MPPT accuracy, facilitating greater energy extraction efficiency and improved overall performance in practical photovoltaic applications. DOI: 10.61416/ceai.v27i4.9541

The transmission delay of information in the network environment degrades the performance of networked control system, so it is necessary to compensate the delay in networked control system. This paper proposes a delay compensation strategy for networked control system based on neural network predictive control. Firstly, the BP neural network is optimized by the Cuckoo search algorithm to establish a delay prediction model to identify the transmission delay. Next, the multi-step delay prediction model is combined with the implicit generalized predictive control algorithm to solve the control increment at future sampling times. Finally, a delay compensation strategy is proposed by setting a buffer in the actuator, selecting appropriate control increments based on the delay situation to improve control accuracy and achieve delay compensation. The simulation comparison of networked gas boiler control system verifies the effectiveness of the proposed networked control system delay compensation strategy. DOI: 10.61416/ceai.v27i1.9287

Recent IT research on prime numbers has focused on generating large primes forcryptography, neglecting the advancement of systematic generation of prime numbers insequence using techniques like sieving. Despite being relegated to educational contexts, sieving techniques hold untapped potential. Much of the existing work remains obscure to contemporaryresearchers, leaving numerous opportunities to enhance the field. This article aims to highlight the relevance of old sieving algorithms, demonstrating that modern approaches and creativity can yield significant progress. DOI: 10.61416/ceai.v26i4.9098

The focus of researchers to develop Unmanned Vehicles, capable of moving on land, water or through air, increased over time. In the last decade the research and development for this domain started to focus on developing and making Unmanned Aerial Vehicles (UAVs) more efficient, observing the huge potential to integrate them in a large set of applications. This paper presents the state of the art of the existing UAV systems, applications, and control strategy implementations on Swarm of UAVs by analyzing and referencing the newest publications in the last few years. A literature review of all relevant papers related to swarms of UAVs and how these multi-agent systems are implemented was conducted. This article highlights the current challenges, solutions and directions that are revealed in the literature. The final goal is to propose a specific control strategy solution for a swarm of drones, based on the conclusions extracted from literature. DOI: 10.61416/ceai.v26i4.9270

Internet of Things (IoT) represents the interconnected network of different intelligent objects that contain sensors, software, and connectivity enables them to collect and exchange data over the internet. Ensuring the security of IoT is crucial in the digital realm as devices are all connected and they manage a lot of sensitive data. This paper proposes a benchmark comparison between a traditional IoT device and an AI accelerated device in order to evaluate the performances of each system in the context of IoT security and determine which platform can be used as a robust AI security detection and prevention system. DOI: 10.61416/ceai.v26i3.8992

Driving style, a critical indicator that affects vehicle fuel economy, its recognition has traditionally relied on onboard sensors and limited vehicle-to-vehicle communication. However, 6G technology facilitates the collection of vast amounts of data, including vital signs like vehicle speed, power sources performance, and real-time traffic conditions, which can be used for real-time driving style recognition. It enables vehicles to make informed decisions about power allocation and fuel consumption, thereby advancing the future of green and efficient transportation. For driving style recognition problem, the principal component analysis (PCA) method is adopted to select the speed and the absolute values of acceleration as driving style identification parameters and the fuzzy-logic controller optimized by genetic algorithm (GA) is designed to identify driving style. Afterwards, the driving style optimal control strategy is realized by matching the recognized driving style with the optimal equivalent factor in each driving condition and the matched equivalent factor is combined with the objective function of ECMS. The effectiveness of proposed driving style based on ECMS is validated by real vehicle test, which indicates that, compared with the strategy without considering driving styles, the proposed driving style recognition based ECMS reduces the hydrogen consumption of FCHEV by 3.7% in the combination of HWFET and UDDS. DOI: 10.61416/ceai.v26i2.9013

Hybrid Energy Source is used to combine the multiple energy source, in order to use their characteristics at their best. A power electronic converter is needed to interface these hybrid energy source with a load. In traditional methods various single input DC-DC power electronic converters are used but it has drawbacks such as, complex design, huge cost and less efficiency. Therefore, it is important to identify the opportunities to cut down the cost and to enhance the performance of the converter. One such possibility is to use dual source DC-DC converter that leads to reductions of filter size, cost and losses. Taking this into consideration, this study explores, the detailed software simulation of three different topologies of dual source DC-DC converters (Topology-I, Topology-II and Topology-III) in MATLAB/Simulink platform. All the three converters can work in buck, boost and bidirectional mode. Furthermore, a performance comparison of these converter topologies are made to check the feasible converter for electric vehicle application. The performance comparison shows that the Topology-III converters has less component count, improved efficiency, high voltage gain, high average output power and compact structure when compared with existing converter topology (I and II).