Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.

In an agricultural system, finding suitable watering, pesticides, and soil content to provide the right nutrients for the right plant remains challenging. Plants cannot speak and cannot ask for the food they require. These problems can be addressed by applying intelligent (fuzzy logic) controllers to IoT devices in order to enhance communication between crops, ground mobile robots, aerial robots, and the entire farm system. The application of fuzzy logic in agriculture is a promising technology that can be used to optimize crop yields and reduce water usage. It was developed based on language and the air properties in agricultural fields. The entire system was simulated in the MATLAB/SIMULINK environment with Cisco Packet Tracer integration. The inputs for the system were soil moisture sensors, temperature sensors, and humidity sensors, and the outputs were pump flow, valve opening, water level, and moisture in the sounding. The obtained results were the output of the valve opening, moisture in the sounding, pump flow rate, outflow, water level, and ADH values, which are 10.00000013 rad/s, 34.72%, 4.494%, 0.025 m³/s, 73.31 cm³, and 750 values, respectively. The outflow rate increase indicates that water is being released from the tanks, and the control signal fluctuates, indicating that the valve is opening.

Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues.

The trajectory tracking is important to make the WMR move autonomously from the starting point to the destination along a predefined time. Implementing of trajectory tracking control is a fundamental part to accomplish its application tasks. In this article a new method by using a hybrid controller has been presented to solve the problem of the trajectory tracking of four mecanum wheeled mobile robot. Proposed controller is depending on modeling of robot kinematic and dynamic equations. The novelty in this work is that, an optimal control system self-tuning parameters based on an optimization algorithm for these models of the mobile robot is utilized. The optimal control type that is used in this work is the Linear Quadratic Regulator (LQR) controller. LQR is used to control the actuator torque that is required in each wheel to achieve the robot task. The parameters of the LQR controller are tuned by using Ant Colony Optimization (ACO). For results simulation, MATLAB/ Simulink is used for circular and infinity shape trajectories. Results show that when the robot follows a circular trajectory, the values of position trajectory error values are reduced to small value (ex=3.218 *10-5m) and (ey= 2.224*10-5m) in xo and yo directions, respectively and remained almost at these values until the end of the simulation time. The maximum orientation error is (= 0.103rad), and convergent to zero after two seconds of the mobile robot movement. Also when the robot follows an infinity shape trajectory, the position trajectory error values are (ey) and () are reduced to small value -4.078 *10-4m and 3.174*10-4rad respectively, while, (ex) is reduced to 5.263*10-4 m after about 15 seconds.

Most current research on motion control and obstacle avoidance for mobile robot (MR) is primarily focused on static obstacle avoidance, with limited attention to detecting as well as avoiding moving obstacle. As a result, significant research is still needed to address the problems associated with movable obstacle avoidance. The difficulty of movable obstacle avoidance is influenced by several factors related to obstacle, including its size, geometric dimensions, velocity, direction of movement, and acceleration. Other parameters related to the kinematics of MR also play a significant role. Therefore, this research aimed to develop a new method of intelligent detection as well as avoidance of movable and static obstacle. To achieve this purpose, wheeled mobile robot (WMR) was equipped with six ultrasonic sensors to detect the distance between the robot and obstacle. Moreover, a new algorithm was developed based on WMR center position (CP), provided by the control system or distances between the obstacle and WMR detected by sensors. This algorithm determined the position and velocity of movable obstacle. According to the outputs related to movable obstacle, WMR avoided collision by altering its path to follow a feasible alternative path, which was planned based on individual priority. This method was simulated in avoidance of static and movable obstacle using MATLAB Simulink program. The results obtained during the analysis showed that the percentage of maximum additional time to avoid the fixed obstacle was 1.15% while movable obstacle was 0.7% of the total time. In addition, the percentage of maximum additional length to avoid the fixed obstacle was 5.52% while movable obstacle was 3.58% of the total desired length. The results were satisfactory compared to previous research, showing that WMR avoided movable obstacle and returned to the desired path faster than in other investigations.