In this paper, a novel Model Reference Adaptive Control (MRAC)-based hybrid control algorithm is presented for the trajectory tracking of a tri-rotor Unmanned Aerial Vehicle (UAV). The mathematical model of the tri-rotor is based on the Newton–Euler formula, whereas the MRAC-based hybrid controller consists of Fuzzy Proportional Integral Derivative (F-PID) and Fuzzy Proportional Derivative (F-PD) controllers. MRAC is used as the main controller for the dynamics, while the parameters of the adaptive controller are fine-tuned by the F-PD controller for the altitude control subsystem and the F-PID controller for the attitude control subsystem of the UAV. The stability of the system is ensured and proven by Lyapunov stability analysis. The proposed control algorithm is tested and verified using computer simulations for the trajectory tracking of the desired path as an input. The effectiveness of our proposed algorithm is compared with F-PID and the Fuzzy Logic Controller (FLC). Our proposed controller exhibits much less steady state error, quick error convergence in the presence of disturbance or noise, and model uncertainties.

A hybrid control scheme is proposed in this paper to achieve the desired (position and altitude) tracking response of an unmanned aerial vehicle (UAV). The tri-rotor-based aircraft is selected to perform the simulations considering that the dynamic model of the UAV is already unstable, under-actuated and nonlinear in behaviour. The designed control scheme is used to produce the rotational velocities of UAV which are consequently handled by the hybrid controller. The hybrid controller consists of model reference adaptive control (MRAC) with regulation, pole-placement and tracking (RST) controller and the entire system stability is dealt with by MIT rules. The presented control scheme is tested via computer-based simulations to follow the desired position and attitude of UAV. The efficiency of the proposed scheme is compared with MRAC-based MIT rule and its validity is further checked with MRAC-based Lyapunov rule. The results show that the presented hybrid controller exhibits robustness, fast error convergence and a zero steady state error in the existence of model uncertainties and disturbances.

In recent years, silver nanoparticles have emerged as potent antimicrobial agents capable of combating extensively drug-resistant pathogenic bacteria that pose serious health risks. The primary aim of our research was to explore the green synthesis of AgNPs using Candida glabrata as an antibacterial agent. A single clinical isolate of Candida glabrata was re-examined via traditional yeast identification methods. Biosynthesis of AgNPs was accomplished by incubating Candida glabrata cell-free supernatant with silver nitrate. AgNP formation was verified by UV-Vis spectroscopy, and the XRD technique assessed the physical properties of the lyophilized AgNPs. EDX and SEM provided insights into the AgNPs’ composition, shape, and size. The antibacterial efficacy was evaluated against pathogenic bacteria through the Agar Well Diffusion method. The formation of AgNPs was evidenced by a shift in color to dark brown. The formation of AgNPs at an absorbance wavelength of 430 nm revealed a polycrystalline structure with an average crystal size of 21.91 nm. The silver constituted 29.50% of the composition and indicated a spherical shape with sizes ranging from 74.96 to 100.40 nm. Significant antimicrobial activity was obtained against pathogenic bacteria. Hence, the proposed research highlights a single-step, cost-effective, and environmentally friendly AgNP synthesis approach that exhibits considerable antibacterial properties.