Quadrotor systems are becoming increasingly popular in various applications due to their maneuverability and versatility. Controlling these systems accurately is crucial to ensure stability and safety. This research paper proposes the implementation of two advanced controllers with integer and fractional order quadrotor systems. The purpose of the study is to enhance the control performance, robustness, and accuracy of the quadrotor system, and to highlight the potential of the proposed approach in modern control engineering. The researchers used simulation studies in MATLAB to verify the effectiveness of their approach. The results demonstrate that the implementation of Sliding Mode Control (SMC) and Fractional Order Sliding Mode Control (FOSMC) with the fractional order quadrotor system outperforms the traditional integer order quadrotor system in terms of control performance, robustness, and accuracy. Overall, the study highlights the potential of fractional order modeling and fractional order control techniques in improving the performance of quadrotor systems, which has significant implications for the advancement of modern control engineering.

Quadrotors, commonly known as drones or unmanned aerial vehicles (UAVs), play an important role in load transportation. The complex vehicles used for transporting loads and surveillance purposes can be replaced through the ease of use and mechanical simplicity of the quadrotors. This study aims to introduce a new way of controlling the slung load system of the quadrotor after applying the fractional-order sliding mode control (FOSMC) method for performance enhancement. This method in the presence of external disturbances is likely to help stabilize and track the quadrotor with minimized swinging of the attached load. The results of this study prove the robustness of FOSMC through analysis, outcomes, and graphical representations that show the effect with various angles for a clear and conceptual understanding. The present study contributes to the literature by designing a robust FOSMC for a quadrotor with the help of external disturbances. The results of this study could be applied to the development of the design of upcoming drones which can increase the efficiency as well as the accuracy of load transportation. Further applications in the fields of rescue, agriculture, construction, research, and advancement of the fraction calculus-based control method are recommended using the FOSMC method.

In this research, we investigate the reinforcement learning-based control strategy for second-order continuous-time multi-agent systems (MASs) subjected to actuator cyberattacks during affine formation maneuvers. In this case, a long-term performance index is created to track the MASs tracking faults using a leader-follower structure. In order to approximate the ideal solution, which is challenging to find for systems vulnerable to cyberattacks during time-varying maneuvers, a critical neural network is used. The distributed control protocol is obtained, and the long-term performance index is minimized, using an actor neural network strengthened with critic signals. The actor-critic neural networks calculate unknown dynamics and the severity of attacks on the MAS actuators. The Nussbaum functions are applied to address this issue since attacks can result in a loss of control direction. The stability of the closed-loop system has been emphasized with the use of a Lyapunov candidate function. The performance of the suggested strategy is then supported by a numerical simulation.

Autonomous mobile robots (AMRs) require robust, efficient path planning to operate safely in complex, often dynamic environments (e.g., logistics, transportation, and healthcare). This systematic review focuses on advanced metaheuristic and learning- and reasoning-based (cognitive) techniques for AMR path planning. Drawing on approximately 230 articles published between 2018 and 2025, we organize the literature into two prominent families, metaheuristic optimization and AI-based navigation, and introduce and apply a unified taxonomy (planning scope, output type, and constraint awareness) to guide the comparative analysis and practitioner-oriented synthesis. We synthesize representative approaches, including swarm- and evolutionary-based planners (e.g., PSO, GA, ACO, GWO), fuzzy and neuro-fuzzy systems, neural methods, and RL/DRL-based navigation, highlighting their operating principles, recent enhancements, strengths, and limitations, and typical deployment roles within hierarchical navigation stacks. Comparative tables and a compact trade-off synthesis summarize capabilities across static/dynamic settings, real-world validation, and hybridization trends. Persistent gaps remain in parameter tuning, safety, and interpretability of learning-enabled navigation; sim-to-real transfer; scalability under real-time compute limits; and limited physical experimentation. Finally, we outline research opportunities and open research questions, covering benchmarking and reproducibility, resource-aware planning, multi-robot coordination, 3D navigation, and emerging foundation models (LLMs/VLMs) for high-level semantic navigation. Collectively, this review provides a consolidated reference and practical guidance for future AMR path-planning research.