This paper is aimed at addressing the tracking control issue for an n-DOF manipulator regardless of unknown friction and unknown control direction. In order to handle the above issues, an adaptive sliding mode control (ASMC) is developed with a Nussbaum function. The sliding mode control (SMC) in the proposed control guarantees the tracking problem and fast responses for the manipulator. Additionally, there are adaptive laws for the robust gain in the SMC to deal with the unknown external disturbance and reduce the chattering effect in the system. In practice, the mistakes in the connection between actuators and drivers, named unknown control direction, cause serious damage to the manipulator. To overcome this issue, the Nussbaum function is multiplied by the ASMC law. A Lyapunov approach is investigated to analyze the stability and robustness of the whole system. Finally, several simulations are implemented on a 3-DOF manipulator and their results are compared with those of the existing controllers to validate the advantages of the proposed method.

In recent years, the research on drones has increased rapidly because of its high applicability in many fields and its great development potential. In the maritime sector too, especially marine rescue, a Drone with a compact size and fast flight speed is an effective solution in search and surveillance, giving quick results and being very convenient. When operating at sea, marine rescue drones are often affected by the environment, especially wind, which leads to turbulence that causes the drone to deviate from its predetermined flight trajectory. To overcome the above problem, the author has proposed the application of a Neural-Fuzzy controller for unmanned marine rescue aircraft presented in this paper introduces a controller that combines neural networks and fuzzy controllers that enhance the efficiency of the drone’s trajectory tracking. The paper presents the mathematics of a quadcopter described by the Newton-Euler equations. Presentation on stable flight control and trajectory control of marine rescue drones. In this paper, Matlab/Simulink is used to describe the operation of the Drone, and the characteristics obtained after using the simulation are used to compare, test, and analyze the system. The obtained results show that the Neural-Fuzzy controller is much more sensitive, more resistant to turbulence, and can be used on different sizes, weights, and configurations of drones without adjusting PID gain.

The sliding mode controller stands out for its exceptional stability, even when the system experiences noise or undergoes time-varying parameter changes. However, designing a sliding mode controller necessitates precise knowledge of the object’s exact model, which is often unattainable in practical scenarios. Furthermore, if the sliding control law’s amplitude becomes excessive, it can lead to undesirable chattering phenomena near the sliding surface. This article presents a new method that uses a special kind of computer program (Radial Basis Function Neural Network) to quickly calculate complex relationships in a robot’s control system. This calculation is combined with a technique called Sliding Mode Control, and Fuzzy Logic is used to measure the size of the control action, all while making sure the system stays stable using Lyapunov stability theory. We tested this new method on a robot arm that can move in three different ways at the same time, showing that it can handle complex, multiple-input, multiple-output systems. In addition, applying LPV combined with Kalman helps reduce noise and the system operates more stably. The manipulator’s response under this controller exhibits controlled overshoot (Rad), with a rise time of approximately 5 ± 3% seconds and a settling error of around 1%. These control results are rigorously validated through simulations conducted using MATLAB/Simulink software version 2022b. This research contributes to the advancement of control strategies for robotic manipulators, offering improved stability and adaptability in scenarios where precise system modeling is challenging.

In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency.

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.