Unmanned Aerial Vehicles (UAVs) are transforming various fields, from surveillance to agriculture, necessitating advanced control systems for effective operation. This study explores three prominent control strategies for UAV trajectory tracking: Proportional-Integral-Derivative (PID) control, Sliding Mode Control (SMC), and Backstepping Control. Each method is enhanced through Gray Wolf Optimization (GWO), a nature-inspired algorithm designed to determine the optimal control gains, thereby maximizing performance in real-world scenarios. The paper begins by providing a thorough overview of each control technique, elucidating their underlying principles and typical applications in UAV systems. We employ GWO to fine-tune the parameters of each controller, enabling a systematic approach to optimization that takes advantage of the algorithm's ability to converge towards global optima. This optimization is critical, as the success of UAV operations heavily relies on precise trajectory tracking, which is inherently influenced by the selected control strategy. To evaluate the effectiveness of each approach, we conduct a series of simulations that track the UAV’s performance across various trajectories. Key performance indicators such as speed, precision, and robustness are meticulously analyzed. The results illustrate significant variances in how each controller performs under different operational conditions, with a detailed discussion of their respective advantages and limitations. For instance, while the PID controller is noted for its simplicity and ease of implementation, it may struggle with robustness in dynamic environments. In contrast, the Sliding Mode Controller exhibits superior resilience to disturbances, yet may require more complex tuning. The Backstepping Control method, on the other hand, demonstrates exceptional precision, particularly in complex maneuvers, but can be computationally intensive. This comparative analysis provides crucial insights for researchers and practitioners in the UAV domain, highlighting the importance of selecting the appropriate control strategy based on mission requirements. By integrating GWO into the optimization process, we pave the way for more efficient and reliable UAV control systems, ultimately contributing to the advancement of autonomous aerial operations. The findings underscore the potential for future work to explore hybrid control approaches that leverage the strengths of each method, further enhancing UAV capabilities in increasingly complex environments.

This study presents a detailed analysis of a pyroelectric detector based on a lithium niobate (LiNbO₃) crystal for optimizing its performance under different geometrical configurations and electrical load conditions. The effect of varying the pyroelectric disk radius and thickness, as well as the electrical load resistance, on voltage, current, power, and temperature profiles, was thoroughly investigated. Results indicate that larger disk radii (up to 5 mm) enhance voltage and current sensitivity, thereby maximizing power output. However, larger radii also lead to slower temperature decay, highlighting the need for efficient thermal management to prevent structural overheating and maintain long-term functionality. Additionally, increasing the disk thickness from 0.01 mm to 0.04 mm results in substantial improvements in voltage, current, and power, with the most significant changes occurring between 0.01 mm and 0.02 mm. Conversely, thicker disks show better heat dissipation, helping mitigate temperature rise. The analysis of varied electrical load resistances reveals that lower resistances (1 kΩ) generate higher power and voltage outputs, while higher resistances reduce the system’s electrical response. These findings underscore the importance of optimizing both geometrical and electrical parameters to enhance the overall performance and thermal stability of pyroelectric detectors in practical applications. These findings provide valuable insights for optimizing pyroelectric sensor performance through geometric and electrical load adjustments. Future work includes the fabrication of the sensor using the optimized parameters, followed by experimental validation to assess its real-world performance.

Abstract - In modern industry, the integration of sensors with advanced artificial intelligence (AI) algorithms is essential for enhancing workflow efficiency and decision-making capabilities. This work introduces two innovative approaches that use these technologies to monitor and analyze liquid properties in real-time, shown in Figure 1. In the first approach (Figure 1 (a)), we designed a cuboid-shaped fluidic cell [1], fabricated from various materials using novel 3D printing techniques, featuring a vibrating membrane at its base with two external piezoelectric actuators attached. The second approach (Figure 1 (b)) employed micromachined plates driven by AlN piezoelectric films, fabricated using MEMS technology, meticulously designed to achieve quasi-free-free vibration in the (0,5) mode [2]. Both types of sensors used a two-port structure, one for actuation and the other for detection. The integration of these devices with AI techniques allowed us to use frequency responses in a range with multiple resonances, highly sensitive to fluid properties [3], while eliminating the need for complex electronics to process and acquire data.

Figure 1. (a) Top and cross-sectional views of the 3D-printed liquid cell with piezoelectric actuators. (b) Portable, low-cost viscometer-densimeter that included a MEMS microresonator, a microcontroller unit, and conditioning electronic circuits. The system incorporated a 3D-printed fluidic cell for injecting liquid into the sensor.

The spectra obtained with the sensors were subjected to various advanced data processing and machine learning techniques, performing an exhaustive search for the optimal combinations of hyperparameters that best fit the sensor data. Convolutional neural networks (CNNs) were found to be highly effective in working with frequency characteristics and estimating the viscosity and density of different types of liquids. In the second approach, these models were implemented on a microcontroller board, which also managed all electronics and communication with the sensor, resulting in a precise, compact, portable, and low-cost device.

Cell-based systems proved effective for monitoring the properties of aqueous solutions, achieving calibration errors below 2% and resolutions of 7.79 · 10^-3 mPa·s for viscosity, and 1.09 · 10^-3 g/mL for density. The microelectromechanical resonator-based instrument was capable to detect very small adulterations in olive oil with other vegetable oils, as low as 2%, with calibration and resolution errors of 0.47% and 0.14 mPa·s for viscosity, and 0.0331% and 9.25 · 10^-5 g/mL for density. The calibration and resolution accuracies obtained were comparable to or exceeded those in the state of the art, and were on par with other commercial laboratory instruments of greater complexity, cost, and stationary nature.

Our findings demonstrate the significant potential of integrating sensors with machine learning techniques to achieve accurate detection of physical properties in fluids and address complex and critical industrial challenges, such as olive oil fraud. These advancements pave the way for the development of next-generation sensors that are not only accurate and reliable but also scalable and adaptable to diverse applications, providing valuable tools for the new industry.

This article presents a comprehensive study on the control of linear speed and angular position in mobile robots using Proportional-Integral-Derivative (PID) controllers, with a focus on optimizing PID gains through metaheuristic optimization techniques. We begin by developing a detailed mathematical model of the robotic system, capturing its dynamics and response characteristics. Utilizing metaheuristic algorithms, such as Genetic Algorithms and Particle Swarm Optimization, we adaptively tune the PID parameters to enhance system performance in varying operational environments. The simulation results demonstrate significant improvements in trajectory tracking accuracy, reduced overshoot, and quicker settling times compared to conventional PID approaches. Additionally, the optimized PID controller showcases robust performance under different conditions, validating the effectiveness of our modeling and optimization strategy. This research not only highlights the potential of metaheuristic methods in fine-tuning PID controllers but also provides valuable insights into the simulation of mobile robotic systems, contributing to advancements in robotic control and navigation technologies.

In photovoltaic (PV) systems, efficient energy extraction is critical, especially under partial shading conditions where multiple local maxima can complicate the search for the true Maximum Power Point (MPP). This paper presents a robust approach to Maximum Power Point Tracking (MPPT) using the Particle Swarm Optimization (PSO) metaheuristic, tailored specifically for partial shading scenarios. The PSO method is advantageous due to its ability to handle the complex, non-linear nature of the power-voltage (P-V) and current-voltage (I-V) characteristics under varying irradiance levels. Unlike conventional MPPT techniques, which may converge to local maxima, the PSO-based method dynamically adjusts the duty cycle of the DC-DC converter, efficiently navigating the search space to locate the global MPP. The proposed method is evaluated through extensive simulations, where it consistently demonstrates superior performance in tracking the true MPP, regardless of the shading pattern. The paper provides a detailed analysis of the P-V and I-V curves under different shading conditions, showcasing how the PSO algorithm outperforms traditional methods in both convergence speed and accuracy. The results indicate a significant improvement in the power output of the PV system, highlighting the effectiveness of PSO in optimizing energy harvest. This study contributes to the growing field of renewable energy by offering a reliable and efficient solution for maximizing power generation in partially shaded PV systems.