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In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CDCARLA technique and compares its performance with Particle Swarm Optimization (PSO) as an efficient evolutionary optimization method. The proposed method has been applied to PID controller design. The simulation results show the superior efficiency and robustness of the proposed method.

In this paper, Lagrangian-based method has been proposed for tuning the parameters of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller. In this method, the five parameters ($K_p$, $K_i$, $K_d$, $\alpha$ and $\beta$) of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller (FOPID) are suitably optimized, and successfully applied to a benchmark stable second-order feedback system. To prove the performance of the proposed method, several state-of-the-art approaches were compared. The computational complexity, robustness and stability analysis has been performed to investigate the performance of all these algorithms. Moreover, the precision and flexibility analysis among all these approaches has also been carried out in this paper. The closed loop response of the second-order bench mark stable plant in Simulink has also been depicted in this paper.

The utilization of unmanned aerial vehicles (UAVs) equipped with advanced machine vision and remote sensing technology has ushered in a transformative era across diverse domains such as agriculture, construction, energy, environmental monitoring, and contemporary medical treatment. Particularly within the realm of modern medical treatment, the integration of drone technology into healthcare service delivery has displayed tremendous promise for enhancing patient outcomes, particularly in geographically challenging areas or during emergent situations.

Quad-rotor UAVs have emerged as prominent contenders owing to their exceptional performance, user-friendly controls, and adaptable functionality. Their quad-rotor configuration enables seamless vertical takeoff and landing, rendering them highly suitable for deployment in demanding environments frequently encountered in the context of modern medical treatment. When combined with cutting-edge machine vision and remote sensing capabilities, quad-rotors exhibit proficiency in executing intricate missions encompassing medical supply delivery, mobile target tracking, anomaly detection, topographical mapping, and even facilitating emergency medical response scenarios. These remarkable capabilities render quad-rotors indispensable tools for healthcare professionals including doctors, nurses, and other dedicated personnel in the provision of high-quality healthcare services within the realm of modern medical treatment.

The design and implementation of a dynamic tracking system predicated on machine vision and remote sensing technology assume paramount importance in ensuring optimal UAV operations in modern medical treatment applications. A meticulously crafted system should possess the capacity to process visual information through sophisticated machine vision algorithms, thereby transforming it into precise control data for the UAV. Employing a remote sensing target detection model serves to accurately locate and identify pertinent information embedded within optical remote sensing imagery, thereby generating a comprehensive output feature map information set. Integration of this information with the cascade PID (Proportion Integral Differential) control algorithm facilitates the realization of a dynamic tracking system tailored specifically for quad-rotor UAVs.

Through meticulous experimentation, the system can be effectively harnessed for portable high-altitude visual detection, thereby endowing healthcare services with promptness and efficiency. For instance, the system can be leveraged to meticulously track the movement of medical supplies during transportation, remotely monitor patients’ vital signs, and even facilitate the expeditious delivery of essential medications or vaccines to individuals situated in remote and inaccessible locations during modern medical treatment. As the demand for healthcare services continues to surge, the prevalence of drones equipped with state-of-the-art machine vision and remote sensing technology within modern medical treatment contexts is expected to increase manifold. Consequently, sustained research and development endeavors dedicated to advancing this technology assume paramount significance, thereby ensuring its seamless and efficacious implementation within the modern medical treatment field.