The present study addresses two representative model-free control strategies namely, model-free intelligent PID (i-PID) and type-2 fuzzy adaptive PID in control of a quadrotor type vertical take-off and landing (VTOL) unmanned aerial vehicle. The objectives of this study are i) to summarize the modeling and flight control methods of quadrotor in some classifications, ii) to investigate merits and demerits of model-free control strategies, iii) to compare the control performance in terms of some quantitative performance criteria. The results illustrate the performance of such methodologies applied to the quadrotor system.

Brushless direct current (BLDC) motors are high efficiency synchronous motors that are employed in a variety of applications due to prominent features such as long operational life, low maintenance requirements, and great dynamic response. BLDC motors are driven by energizing the motor stator windings with an inverter circuit. To commute the inverter switches, the trapezoidal (120°) method is generally used by considering the back electromagnetic force induced on unenergized phase of BLDC. Furthermore, depending on the application requirements, alternative commutation modes (CM) such as 180°, 150°, and sinusoidal-based approaches are utilized. In the literature, the performance comparison of some CMs was studied for two-phase on operation and three-phase on operation mode as a switching pattern. However, only a few research assessed the performance of two or three commutation modes simultaneously. In this study, the performance comparison of pulse width modulation (PWM) based commutation modes are examined by considering 120°, 180°, 150° modes, and sinusoidal PWM. In this scope, it is the first time that direct digital synthesis (DDS) is addressed as a BLDC control algorithm in a performance comparison study. In experimental studies, a simple and a low-cost drive circuit is designed to acquire the case results. According to the results, the proposed DDS-based sine commutation method is more efficient than other commutation methods and it has lower power consumption at both low and high speeds also a wider operable speed range.

In this study, an intelligent adaptive interaction torque control for a pneumatically actuated forearm rehabilitation robot has been proposed. The main objective is to provide a haptic environment that ensures stable interaction torque fields at changing levels. To achieve this goal, a cascade fuzzy adaptive controller, that is specifically tailored to handle varying levels of interaction torque and ensure stability throughout the rehabilitation process, has been designed. To improve the efficiency of the controller, non-linear friction torque identification of the pneumatic actuator based on changing operating conditions has been conducted. Parallel to this, a user motion intention detection algorithm has been designed to provide compliant, safe and suitable human-robot interactions. The disturbance cases have been considered to make the system robust to unknown conditions. Stability analysis has been performed, specifically focusing on the boundary-input boundary-output (BIBO) stability conditions. In order to demonstrate the superior performance of the proposed cascade fuzzy adaptive algorithm, a cascade PID algorithm has also been meticulously designed for comparison. Numerous experimental validation tests involving a healthy user were conducted in a Hardware-in-the-Loop environment, focusing on torque trajectory tracking performance. The proposed control technique exhibited improved convergence dynamics compared to the cascade PID algorithm, yielding mean absolute error levels of 0.0218 Nm and 0.099 Nm for target interaction torque under disturbance-free and disturbed conditions, respectively.

This research explores the dynamics of vegetation patterns under changing environmental conditions, considering the United Nations Sustainable Development Goal 15: “Protect, restore, and promote the sustainable use of terrestrial ecosystems; combat desertification; halt and reverse land degradation; and prevent biodiversity loss”. In this context, this study presents a modeling and nonlinear analysis framework for plant–soil-moisture interactions, including Holling-II functional response and hyperbolic mortality models. The primary goal is to explore how nonlinear soil–water interactions influence vegetation patterns in semi-arid ecosystems. Moreover, the influence of nonlinear soil–water interaction on the establishment of population patterns is investigated. The formation and evolution of these patterns are explored using theoretical analysis and numerical simulations, as well as important factors and critical thresholds. These insights are crucial for addressing desertification, a key challenge in semi-arid regions that threatens biodiversity, ecosystem services, and sustainable land management. The model, which includes environmental parameters such as rainfall, plant growth rates, and soil moisture, was tested using both theoretical analysis and numerical simulations. These characteristics are carefully adjusted to find important thresholds influencing the danger of desertification. Simulation scenarios, run under set initial conditions and varying parameters, yield useful insights into the pattern of patch development under dynamically changing environmental conditions. The findings revealed that changes in environmental conditions, such as rainfall and plant growth rates, prompted Hopf bifurcation, resulting in the production of three distinct patterns: a dotted pattern, a striped pattern, and a combination of both. The creation of these patterns provides essential information about the sustainability of environmental equilibrium. The variation curve of the average plant biomass reveals that the biomass fluctuates around a constant period, with the amplitude initially increasing, then decreasing, and gradually stabilizing. This research provides a solid foundation for addressing desertification risks, using water resources responsibly, and contributing to a better understanding of ecosystem stability.