This paper studies the delay consensus margin and its bounds for second-order multiagent systems to achieve robust consensus with respect to uncertain delays varying within a range. This paper attempts to answer the question: What is the largest delay range within which a control protocol is able to achieve and maintain the consensus? We consider second-order agents with unstable poles, which communicate over an undirected network topology, and derive explicit bounds on the delay consensus margin. The results show that the consensuability robustness of such unstable agents depends on the pole locations of the agents, as well as on the eigenratio of the network graph.

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework’s effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting.

Unmanned aerial vehicles (UAVs), particularly quadcopters, have several medical, agriculture, surveillance, and security applications. However, the use of this innovative technology for civilian applications is still very limited in low‐income countries due to the high cost, whereas low‐cost controllers available in the market are often tuned using the hit and trial approach and are limited for specific applications. This paper addresses this issue and presents a novel proof of concept (POC) low‐cost quadcopter UAV design approach using a systematic Model‐Based Design (MBD) method for mathematical modeling, simulation, real‐time testing, and prototyping. The quadcopter dynamic model is developed, and controllers are designed using Proportional Integral, and Derivative (PID), Pole Placement, and Linear Quadratic Regulator (LQR) control strategies. The stability of the controllers is also checked using Lyapunov stability analysis. For verification and validation (V&V) of the design, Software‐in‐the‐Loop, Processor‐in‐the‐Loop, Hardware‐in‐the‐loop testing, and Rapid Control Prototyping have been performed. The V&V methods of the MBD approach showed practically valid results with a stable flight of the quadcopter prototype. The proposed low‐cost POC quadcopter design approach can be easily modified to have enhanced features, and quadcopters with different design parameters can be assembled using this approach for a diverse range of applications.

In this paper, a hybrid fault-tolerant control (FTC) system for quadcopter unmanned aerial vehicles (UAVs) is proposed to counteract the deterioration of the performance of the quadcopter due to motor faults. A robust and adaptive approach to controlling fault conditions is simulated by combining an integral back-stepping controller for translational motion and a nonlinear observer-based sliding-mode controller for rotational motion, and then implemented on an FPGA. Finally, motor faults are treated as disturbances and are successfully compensated by the controller to ensure safe and high-performance flight. Simulations were taken at 0%, 10%, 30%, and 50% motor faults to test how effective the proposed FTC system is. After simulations, the controller’s real-time performance and reliability were validated through hardware-in-the-loop (HIL) experiments. The results validated that the proposed hybrid controller can guarantee stable flight and precision tracking of the desired trajectory when any single motor fails up to the order of 50%. It shows that the controller is of high fault tolerance and robustness, which will be a potential solution for improving the reliability of UAVs in fault-prone conditions.