Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled using the developed ANFIS-PID. Three methods are examined in the developed ANFIS-PID controller: grid partitioning (genfis1), subtractive clustering (genfis2), and fuzzy C-means clustering (genfis3). The results show that the added cam can improve the gripping strength and that the ANFIS-PID model effectively handles the rise time and supported settling time. The developed ANFIS-PID controller demonstrates more efficient performance than Fuzzy-PID and traditional tuned-PID controllers. This proposed controller does not achieve any overshoot, and the rise time is improved by approximately 50–51%, and the steady-state error is improved by 75–95%, compared with Fuzzy-PID and tuned PID controllers. Moreover, the developed ANFIS-PID controller provides more stability for a wide range of set point displacements—0.05 cm, 0.5 cm, and 1.5 cm—during the testing period. The developed ANFIS-PID controller is not affected by disturbance, making it well suited for robotic gripper designs. Grip force control is also investigated using the proposed ANFIS-PID controller and compared with the Fuzzy-PID in three scenarios. The result from this force control proves objects’ higher actual gripping performance by using the proposed ANFIS-PID.

The process of grasping and diverting loads is essential in many industrial applications. Robotic grippers have significantly evolved over the last three decades to carry out these operations. Robotic gripper design focuses primarily on the interaction between the object and the grasping finger. In this study, a developed robotic gripper design by adding a cam was prepared using MATLAB Simulink and a fuzzy-PID controller and derivative of the required mathematical equations. The developed gripper investigated the follower path according to the cam surface curve, which was not included in previous studies. The provided gripper model is designed uncomplicated, but it can perform particular tasks that conventional grippers may not be able to perform with the same accuracy and efficiency. The model has been tested under many complex and erratic operational conditions, and its robustness has appeared clearly from the simulation results. The results showed the power of the proposed gripper mechanism and its intelligent controller concerning the adequate reduction in the rise time, maximum percentage overshoot, steady-state error, and settling time.

The control of liquid levels is one of the most important topics in many industrial applications. In this work, a liquid level control system model was built in a water system consisting of 2-tanks. The control system was designed using intelligent techniques, which were represented by adopting Fuzzy Logic to adjust the important factors in the PID controller. Also, the design factors for the system were chosen, such as the resistance of the valves to flow and the capacity of the tanks, using a genetic algorithm with multiple-objectives optimization. The objectives were to derive mathematical equations for the damping factor, settling time, and steady-state error. The results of optimization have resulted in obtaining a set of design factors for the system that enables an acceptable response based on the application used. Additionally, the design of the main controller is in two types based on the membership function, the number of rules, and the other factors. The results showed that the controller used can reach an acceptable rise time and a significantly lower maximum overshoot compared to the comparable controller.

Some engineering applications requires constant engine speed such as power generators, production lines ..etc. The current paper focuses on adding a new closed loop based on engine torque. Load cells can be used to measure the torque of load applied , the electrical signal is properly handled to manipulate a special fuel actuator to compensate for the reduction in engine speed. The speed loop still acts as the most outer closed loop. This method leads to rapid speed compensation and lead control action.